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Furthermore, humans also report a", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 141, + 455, + 453, + 466 + ], + "spans": [ + { + "bbox": [ + 141, + 455, + 453, + 466 + ], + "score": 1.0, + "content": "strong subjective preference to partnering with FCP agents over all baselines.", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 14.5, + "bbox_fs": [ + 141, + 246, + 471, + 466 + ] + }, + { + "type": "title", + "bbox": [ + 108, + 487, + 190, + 500 + ], + "lines": [ + { + "bbox": [ + 105, + 486, + 192, + 502 + ], + "spans": [ + { + "bbox": [ + 105, + 486, + 192, + 502 + ], + "score": 1.0, + "content": "1 Introduction", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 25 + }, + { + "type": "text", + "bbox": [ + 107, + 512, + 505, + 600 + ], + "lines": [ + { + "bbox": [ + 106, + 512, + 505, + 524 + ], + "spans": [ + { + "bbox": [ + 106, + 512, + 505, + 524 + ], + "score": 1.0, + "content": "Generating agents which collaborate with novel partners is a longstanding challenge for Artificial", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 522, + 505, + 536 + ], + "spans": [ + { + "bbox": [ + 105, + 522, + 434, + 536 + ], + "score": 1.0, + "content": "Intelligence (AI) [4, 16, 37, 52]. Achieving ad-hoc, zero-shot coordination [31,", + "type": "text" + }, + { + "bbox": [ + 434, + 523, + 451, + 534 + ], + "score": 0.26, + "content": "\\bar { 6 6 } ]", + "type": "inline_equation" + }, + { + "bbox": [ + 451, + 522, + 505, + 536 + ], + "score": 1.0, + "content": "is especially", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 106, + 533, + 505, + 547 + ], + "spans": [ + { + "bbox": [ + 106, + 533, + 505, + 547 + ], + "score": 1.0, + "content": "important in situations where an AI must generalize to novel human partners [6, 61]. Many successful", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 544, + 505, + 558 + ], + "spans": [ + { + "bbox": [ + 105, + 544, + 505, + 558 + ], + "score": 1.0, + "content": "approaches have employed human models, either constructed explicitly [14, 35, 53] or learnt implicitly", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 108, + 556, + 506, + 567 + ], + "spans": [ + { + "bbox": [ + 108, + 556, + 506, + 567 + ], + "score": 1.0, + "content": "[12, 60]. By contrast, recent work in competitive domains has shown that it is possible to reach human-", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 566, + 505, + 579 + ], + "spans": [ + { + "bbox": [ + 105, + 566, + 505, + 579 + ], + "score": 1.0, + "content": "level using model-free reinforcement learning (RL) without human data, via self-play [8, 9, 63, 64].", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 577, + 505, + 591 + ], + "spans": [ + { + "bbox": [ + 105, + 577, + 505, + 591 + ], + "score": 1.0, + "content": "This begs the question: Can model-free RL without human data generate agents that can collaborate", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 106, + 589, + 188, + 600 + ], + "spans": [ + { + "bbox": [ + 106, + 589, + 188, + 600 + ], + "score": 1.0, + "content": "with novel humans?", + "type": "text" + } + ], + "index": 33 + } + ], + "index": 29.5, + "bbox_fs": [ + 105, + 512, + 506, + 600 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 605, + 505, + 693 + ], + "lines": [ + { + "bbox": [ + 106, + 605, + 505, + 617 + ], + "spans": [ + { + "bbox": [ + 106, + 605, + 505, + 617 + ], + "score": 1.0, + "content": "We seek an answer to this question in the space of common-payoff games, where all agents work", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 616, + 505, + 628 + ], + "spans": [ + { + "bbox": [ + 105, + 616, + 505, + 628 + ], + "score": 1.0, + "content": "towards a shared goal and receive the same reward. Self-play (SP), in which an agent learns from", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 627, + 505, + 639 + ], + "spans": [ + { + "bbox": [ + 105, + 627, + 505, + 639 + ], + "score": 1.0, + "content": "repeated games played against copies of itself, does not produce agents that generalize well to novel", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 637, + 505, + 650 + ], + "spans": [ + { + "bbox": [ + 105, + 637, + 505, + 650 + ], + "score": 1.0, + "content": "co-players [10, 11, 21, 44]. Intuitively, this is because agents trained in self-play only ever need to", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 649, + 505, + 661 + ], + "spans": [ + { + "bbox": [ + 105, + 649, + 505, + 661 + ], + "score": 1.0, + "content": "coordinate with themselves, and so make for brittle and stubborn collaborators with new partners", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 659, + 505, + 672 + ], + "spans": [ + { + "bbox": [ + 105, + 659, + 505, + 672 + ], + "score": 1.0, + "content": "who act differently. Population play (PP) trains a population of agents, all of whom interact with each", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 669, + 506, + 684 + ], + "spans": [ + { + "bbox": [ + 105, + 669, + 130, + 684 + ], + "score": 1.0, + "content": "other", + "type": "text" + }, + { + "bbox": [ + 130, + 670, + 147, + 682 + ], + "score": 0.65, + "content": "\\pmb { \\| 3 9 \\| }", + "type": "inline_equation" + }, + { + "bbox": [ + 148, + 669, + 506, + 684 + ], + "score": 1.0, + "content": ". While PP can generate agents capable of cooperation with humans in competitive team", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 681, + 505, + 695 + ], + "spans": [ + { + "bbox": [ + 105, + 683, + 134, + 693 + ], + "score": 1.0, + "content": "games", + "type": "text" + }, + { + "bbox": [ + 134, + 682, + 152, + 693 + ], + "score": 0.43, + "content": "\\textcircled { 1 3 4 } \\textcircled { 1 }", + "type": "inline_equation" + }, + { + "bbox": [ + 152, + 681, + 505, + 695 + ], + "score": 1.0, + "content": ", it still fails to produce robust partners for novel humans in pure common-payoff settings", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 106, + 237, + 505, + 252 + ], + "spans": [ + { + "bbox": [ + 106, + 237, + 505, + 252 + ], + "score": 1.0, + "content": "[12]. PP in common-payoff settings naturally encourages agents to play the same way, reducing", + "type": "text", + "cross_page": true + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 249, + 410, + 262 + ], + "spans": [ + { + "bbox": [ + 106, + 250, + 389, + 262 + ], + "score": 1.0, + "content": "strategic diversity and producing agents not so different from self-play", + "type": "text", + "cross_page": true + }, + { + "bbox": [ + 390, + 249, + 407, + 261 + ], + "score": 0.63, + "content": "\\dot { \\mathbb { B } } \\dot { \\mathbb { 4 } } \\mathbb { I }", + "type": "inline_equation", + "cross_page": true + }, + { + "bbox": [ + 408, + 250, + 410, + 262 + ], + "score": 1.0, + "content": ".", + "type": "text", + "cross_page": true + } + ], + "index": 7 + } + ], + "index": 37.5, + "bbox_fs": [ + 105, + 605, + 506, + 695 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "image", + "bbox": [ + 117, + 70, + 493, + 187 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 117, + 70, + 493, + 187 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 117, + 70, + 493, + 187 + ], + "spans": [ + { + "bbox": [ + 117, + 70, + 493, + 187 + ], + "score": 0.97, + "type": "image", + "image_path": "70b2b8c1e9478aed36abef766805e05c03561ba29533d4a7de7ad061fa2d261f.jpg" + } + ] + } + ], + "index": 1, + "virtual_lines": [ + { + "bbox": [ + 117, + 70, + 493, + 109.0 + ], + "spans": [], + "index": 0 + }, + { + "bbox": [ + 117, + 109.0, + 493, + 148.0 + ], + "spans": [], + "index": 1 + }, + { + "bbox": [ + 117, + 148.0, + 493, + 187.0 + ], + "spans": [], + "index": 2 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 107, + 194, + 505, + 229 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 194, + 505, + 207 + ], + "spans": [ + { + "bbox": [ + 106, + 194, + 505, + 207 + ], + "score": 1.0, + "content": "Figure 1: In this work, we evaluate a variety of agent training methods (Section 2) in zero-shot", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 106, + 205, + 505, + 218 + ], + "spans": [ + { + "bbox": [ + 106, + 205, + 505, + 218 + ], + "score": 1.0, + "content": "coordination with agents (Section 4). We then run a human-agent collaborative study designed to", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 217, + 300, + 229 + ], + "spans": [ + { + "bbox": [ + 106, + 217, + 300, + 229 + ], + "score": 1.0, + "content": "elicit human preferences over agents (Section 5)", + "type": "text" + } + ], + "index": 5 + } + ], + "index": 4 + } + ], + "index": 2.5 + }, + { + "type": "text", + "bbox": [ + 108, + 239, + 503, + 262 + ], + "lines": [ + { + "bbox": [ + 106, + 237, + 505, + 252 + ], + "spans": [ + { + "bbox": [ + 106, + 237, + 505, + 252 + ], + "score": 1.0, + "content": "[12]. PP in common-payoff settings naturally encourages agents to play the same way, reducing", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 249, + 410, + 262 + ], + "spans": [ + { + "bbox": [ + 106, + 250, + 389, + 262 + ], + "score": 1.0, + "content": "strategic diversity and producing agents not so different from self-play", + "type": "text" + }, + { + "bbox": [ + 390, + 249, + 407, + 261 + ], + "score": 0.63, + "content": "\\dot { \\mathbb { B } } \\dot { \\mathbb { 4 } } \\mathbb { I }", + "type": "inline_equation" + }, + { + "bbox": [ + 408, + 250, + 410, + 262 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 7 + } + ], + "index": 6.5 + }, + { + "type": "text", + "bbox": [ + 107, + 266, + 505, + 354 + ], + "lines": [ + { + "bbox": [ + 105, + 265, + 505, + 279 + ], + "spans": [ + { + "bbox": [ + 105, + 265, + 505, + 279 + ], + "score": 1.0, + "content": "Our approach starts with the intuition that the key to producing robust agent collaborators is exposure", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 277, + 505, + 290 + ], + "spans": [ + { + "bbox": [ + 105, + 277, + 505, + 290 + ], + "score": 1.0, + "content": "to diverse training partners. We find that a surprisingly simple strategy is effective in generating", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 288, + 506, + 301 + ], + "spans": [ + { + "bbox": [ + 105, + 288, + 222, + 301 + ], + "score": 1.0, + "content": "sufficient diversity. We train", + "type": "text" + }, + { + "bbox": [ + 223, + 289, + 233, + 298 + ], + "score": 0.78, + "content": "N", + "type": "inline_equation" + }, + { + "bbox": [ + 233, + 288, + 506, + 301 + ], + "score": 1.0, + "content": "self-play agents varying only their random seed for neural network", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 298, + 505, + 312 + ], + "spans": [ + { + "bbox": [ + 105, + 298, + 505, + 312 + ], + "score": 1.0, + "content": "initialization. Periodically during training, we save agent “checkpoints” representing their strategy", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 310, + 505, + 322 + ], + "spans": [ + { + "bbox": [ + 105, + 310, + 505, + 322 + ], + "score": 1.0, + "content": "at that point in time. Then, we train an agent partner as the best-response to both the fully-trained", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 321, + 505, + 333 + ], + "spans": [ + { + "bbox": [ + 105, + 321, + 505, + 333 + ], + "score": 1.0, + "content": "agents and their past checkpoints. The different checkpoints simulate different skill levels, and the", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 331, + 505, + 344 + ], + "spans": [ + { + "bbox": [ + 105, + 331, + 505, + 344 + ], + "score": 1.0, + "content": "different random seeds simulate breaking symmetries in different ways. We refer to this agent training", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 342, + 487, + 355 + ], + "spans": [ + { + "bbox": [ + 105, + 342, + 202, + 355 + ], + "score": 1.0, + "content": "procedure as Fictitious", + "type": "text" + }, + { + "bbox": [ + 202, + 343, + 216, + 353 + ], + "score": 0.26, + "content": "\\mathbf { C o }", + "type": "inline_equation" + }, + { + "bbox": [ + 216, + 342, + 487, + 355 + ], + "score": 1.0, + "content": "-Play (FCP) for its relationship to fictitious self-play [7, 27, 28, 69].", + "type": "text" + } + ], + "index": 15 + } + ], + "index": 11.5 + }, + { + "type": "text", + "bbox": [ + 106, + 359, + 505, + 457 + ], + "lines": [ + { + "bbox": [ + 106, + 358, + 506, + 371 + ], + "spans": [ + { + "bbox": [ + 106, + 358, + 506, + 371 + ], + "score": 1.0, + "content": "We evaluate FCP in a fully-observable two-player common-payoff collaborative cooking simulator.", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 369, + 506, + 383 + ], + "spans": [ + { + "bbox": [ + 105, + 369, + 237, + 383 + ], + "score": 1.0, + "content": "Based on the game Overcooked", + "type": "text" + }, + { + "bbox": [ + 237, + 369, + 254, + 381 + ], + "score": 0.63, + "content": "\\boldsymbol { \\left[ \\left[ 2 5 \\right] \\right] }", + "type": "inline_equation" + }, + { + "bbox": [ + 255, + 369, + 506, + 383 + ], + "score": 1.0, + "content": ", it has recently been proposed as a coordination challenge for", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 380, + 505, + 394 + ], + "spans": [ + { + "bbox": [ + 105, + 380, + 505, + 394 + ], + "score": 1.0, + "content": "AI [12, 50, 70]. State-of-the-art performance in producing agents capable of generalization to novel", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 391, + 505, + 405 + ], + "spans": [ + { + "bbox": [ + 105, + 391, + 207, + 405 + ], + "score": 1.0, + "content": "humans was achieved in", + "type": "text" + }, + { + "bbox": [ + 207, + 391, + 225, + 403 + ], + "score": 0.66, + "content": "[ \\mathbb { 1 2 } ]", + "type": "inline_equation" + }, + { + "bbox": [ + 226, + 391, + 505, + 405 + ], + "score": 1.0, + "content": "via behavioral cloning (BC) of human data. More precisely, BC was", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 402, + 505, + 416 + ], + "spans": [ + { + "bbox": [ + 105, + 402, + 505, + 416 + ], + "score": 1.0, + "content": "used to produce models that can stand in as human proxies during training in simulation, a method", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 104, + 411, + 505, + 427 + ], + "spans": [ + { + "bbox": [ + 104, + 411, + 505, + 427 + ], + "score": 1.0, + "content": "we call behavioral cloning play (BCP). We demonstrate that FCP outperforms BCP in generalizing to", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 423, + 505, + 438 + ], + "spans": [ + { + "bbox": [ + 105, + 423, + 505, + 438 + ], + "score": 1.0, + "content": "both novel agent and human partners, and that humans express a significant preference for partnering", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 435, + 505, + 448 + ], + "spans": [ + { + "bbox": [ + 106, + 435, + 505, + 448 + ], + "score": 1.0, + "content": "with FCP over BCP. Our method avoids the cost and potential privacy concerns of collecting human", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 446, + 403, + 458 + ], + "spans": [ + { + "bbox": [ + 106, + 446, + 403, + 458 + ], + "score": 1.0, + "content": "data for training, while achieving better outcomes for humans at test time.", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 20 + }, + { + "type": "text", + "bbox": [ + 107, + 462, + 358, + 474 + ], + "lines": [ + { + "bbox": [ + 106, + 461, + 360, + 475 + ], + "spans": [ + { + "bbox": [ + 106, + 461, + 360, + 475 + ], + "score": 1.0, + "content": "We summarize the novel contributions of this paper as follows:", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 25 + }, + { + "type": "text", + "bbox": [ + 129, + 483, + 505, + 581 + ], + "lines": [ + { + "bbox": [ + 129, + 483, + 505, + 496 + ], + "spans": [ + { + "bbox": [ + 129, + 483, + 505, + 496 + ], + "score": 1.0, + "content": "1. We propose Fictitious Co-Play (FCP) to train agents capable of zero-shot coordination with", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 141, + 494, + 230, + 506 + ], + "spans": [ + { + "bbox": [ + 141, + 494, + 230, + 506 + ], + "score": 1.0, + "content": "humans (Section 2.1).", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 128, + 508, + 506, + 521 + ], + "spans": [ + { + "bbox": [ + 128, + 508, + 506, + 521 + ], + "score": 1.0, + "content": "2. We demonstrate that FCP agents generalize better than SP, PP, and BCP in zero-shot", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 141, + 519, + 381, + 532 + ], + "spans": [ + { + "bbox": [ + 141, + 519, + 381, + 532 + ], + "score": 1.0, + "content": "coordination with a variety of held-out agents (Section 4.2).", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 128, + 532, + 506, + 545 + ], + "spans": [ + { + "bbox": [ + 128, + 532, + 506, + 545 + ], + "score": 1.0, + "content": "3. 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State-of-the-art performance in producing agents capable of generalization to novel", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 391, + 505, + 405 + ], + "spans": [ + { + "bbox": [ + 105, + 391, + 207, + 405 + ], + "score": 1.0, + "content": "humans was achieved in", + "type": "text" + }, + { + "bbox": [ + 207, + 391, + 225, + 403 + ], + "score": 0.66, + "content": "[ \\mathbb { 1 2 } ]", + "type": "inline_equation" + }, + { + "bbox": [ + 226, + 391, + 505, + 405 + ], + "score": 1.0, + "content": "via behavioral cloning (BC) of human data. 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Our method avoids the cost and potential privacy concerns of collecting human", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 446, + 403, + 458 + ], + "spans": [ + { + "bbox": [ + 106, + 446, + 403, + 458 + ], + "score": 1.0, + "content": "data for training, while achieving better outcomes for humans at test time.", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 20, + "bbox_fs": [ + 104, + 358, + 506, + 458 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 462, + 358, + 474 + ], + "lines": [ + { + "bbox": [ + 106, + 461, + 360, + 475 + ], + "spans": [ + { + "bbox": [ + 106, + 461, + 360, + 475 + ], + "score": 1.0, + "content": "We summarize the novel contributions of this paper as follows:", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 25, + "bbox_fs": [ + 106, + 461, + 360, + 475 + ] + }, + { + "type": "list", + "bbox": [ + 129, + 483, + 505, + 581 + ], + "lines": [ + { + "bbox": [ + 129, + 483, + 505, + 496 + ], + "spans": [ + { + "bbox": [ + 129, + 483, + 505, + 496 + ], + "score": 1.0, + "content": "1. We propose Fictitious Co-Play (FCP) to train agents capable of zero-shot coordination with", + "type": "text" + } + ], + "index": 26, + "is_list_start_line": true + }, + { + "bbox": [ + 141, + 494, + 230, + 506 + ], + "spans": [ + { + "bbox": [ + 141, + 494, + 230, + 506 + ], + "score": 1.0, + "content": "humans (Section 2.1).", + "type": "text" + } + ], + "index": 27, + "is_list_end_line": true + }, + { + "bbox": [ + 128, + 508, + 506, + 521 + ], + "spans": [ + { + "bbox": [ + 128, + 508, + 506, + 521 + ], + "score": 1.0, + "content": "2. 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Self-play (SP) where an agent", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 236, + 506, + 249 + ], + "spans": [ + { + "bbox": [ + 105, + 236, + 506, + 249 + ], + "score": 1.0, + "content": "learns with itself, population-play (PP) where a population of agents are co-trained together, and", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 246, + 505, + 260 + ], + "spans": [ + { + "bbox": [ + 105, + 246, + 505, + 260 + ], + "score": 1.0, + "content": "behavioral cloning play (BCP) where data from human games is used to create a behaviorally", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 258, + 504, + 271 + ], + "spans": [ + { + "bbox": [ + 105, + 258, + 493, + 271 + ], + "score": 1.0, + "content": "cloned agent with which an RL agent is then trained. In our method, Fictitious Co-Play (FCP),", + "type": "text" + }, + { + "bbox": [ + 493, + 258, + 504, + 268 + ], + "score": 0.68, + "content": "N", + "type": "inline_equation" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 269, + 505, + 282 + ], + "spans": [ + { + "bbox": [ + 106, + 269, + 505, + 282 + ], + "score": 1.0, + "content": "self-play agents are trained independently and checkpointed throughout training. 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Since these partners are trained independently, they can arrive at different arbitrary", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 106, + 382, + 505, + 394 + ], + "spans": [ + { + "bbox": [ + 106, + 382, + 505, + 394 + ], + "score": 1.0, + "content": "conventions for breaking symmetries. To allow the pool to represent different skill levels, we use", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 106, + 393, + 506, + 406 + ], + "spans": [ + { + "bbox": [ + 106, + 393, + 506, + 406 + ], + "score": 1.0, + "content": "multiple checkpoints of each self-play partner throughout training. The final checkpoint represents a", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 403, + 506, + 417 + ], + "spans": [ + { + "bbox": [ + 105, + 403, + 506, + 417 + ], + "score": 1.0, + "content": "fully-trained “skillful” partner, while earlier checkpoints represent less skilled partners. 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Self-play (SP), where agents learn solely through interaction with themselves.", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 128, + 573, + 507, + 588 + ], + "spans": [ + { + "bbox": [ + 128, + 573, + 507, + 588 + ], + "score": 1.0, + "content": "2. Population-play (PP), where a population of agents are co-trained through random pairings.", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 128, + 587, + 506, + 602 + ], + "spans": [ + { + "bbox": [ + 128, + 587, + 506, + 602 + ], + "score": 1.0, + "content": "3. Behavioral cloning play (BCP), where an agent is trained with a BC model of a human [12].", + "type": "text" + } + ], + "index": 31 + } + ], + "index": 30 + }, + { + "type": "text", + "bbox": [ + 110, + 608, + 473, + 620 + ], + "lines": [ + { + "bbox": [ + 107, + 607, + 474, + 621 + ], + "spans": [ + { + "bbox": [ + 107, + 607, + 474, + 621 + ], + "score": 1.0, + "content": "We also evaluate three variations on FCP to better understand the conditions for its success:", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 32 + }, + { + "type": "text", + "bbox": [ + 130, + 627, + 505, + 723 + ], + "lines": [ + { + "bbox": [ + 129, + 627, + 505, + 641 + ], + "spans": [ + { + "bbox": [ + 129, + 627, + 505, + 641 + ], + "score": 1.0, + "content": "1. 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In the first stage, we train a diverse pool of partners. To", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 360, + 505, + 373 + ], + "spans": [ + { + "bbox": [ + 106, + 360, + 432, + 373 + ], + "score": 1.0, + "content": "allow the pool to represent different symmetry breaking conventions, we train", + "type": "text" + }, + { + "bbox": [ + 432, + 361, + 443, + 370 + ], + "score": 0.79, + "content": "N", + "type": "inline_equation" + }, + { + "bbox": [ + 443, + 360, + 505, + 373 + ], + "score": 1.0, + "content": "partner agents", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 371, + 505, + 384 + ], + "spans": [ + { + "bbox": [ + 105, + 371, + 505, + 384 + ], + "score": 1.0, + "content": "in self-play. Since these partners are trained independently, they can arrive at different arbitrary", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 106, + 382, + 505, + 394 + ], + "spans": [ + { + "bbox": [ + 106, + 382, + 505, + 394 + ], + "score": 1.0, + "content": "conventions for breaking symmetries. To allow the pool to represent different skill levels, we use", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 106, + 393, + 506, + 406 + ], + "spans": [ + { + "bbox": [ + 106, + 393, + 506, + 406 + ], + "score": 1.0, + "content": "multiple checkpoints of each self-play partner throughout training. The final checkpoint represents a", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 403, + 506, + 417 + ], + "spans": [ + { + "bbox": [ + 105, + 403, + 506, + 417 + ], + "score": 1.0, + "content": "fully-trained “skillful” partner, while earlier checkpoints represent less skilled partners. Notably, by", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 106, + 415, + 506, + 427 + ], + "spans": [ + { + "bbox": [ + 106, + 415, + 506, + 427 + ], + "score": 1.0, + "content": "using multiple checkpoints per partner, this additional diversity in skill incurs no extra training cost.", + "type": "text" + } + ], + "index": 19 + } + ], + "index": 15.5, + "bbox_fs": [ + 105, + 338, + 506, + 427 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 431, + 505, + 497 + ], + "lines": [ + { + "bbox": [ + 105, + 431, + 506, + 444 + ], + "spans": [ + { + "bbox": [ + 105, + 431, + 506, + 444 + ], + "score": 1.0, + "content": "In the second stage, we train an FCP agent as the best response to the pool of diverse partners created", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 442, + 506, + 455 + ], + "spans": [ + { + "bbox": [ + 105, + 442, + 506, + 455 + ], + "score": 1.0, + "content": "in the first stage. Importantly, the partner parameters are frozen and thus FCP must learn to adapt to", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 104, + 452, + 506, + 466 + ], + "spans": [ + { + "bbox": [ + 104, + 452, + 506, + 466 + ], + "score": 1.0, + "content": "partners, rather than expect partners to adapt to it. In this way, FCP agents are prepared to follow the", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 464, + 506, + 477 + ], + "spans": [ + { + "bbox": [ + 105, + 464, + 506, + 477 + ], + "score": 1.0, + "content": "lead of human partners, and learn a general policy across a range of strategies and skills. We call our", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 475, + 505, + 487 + ], + "spans": [ + { + "bbox": [ + 106, + 475, + 505, + 487 + ], + "score": 1.0, + "content": "method “fictitious” co-play for its relationship to fictitious self-play in which competitive agents are", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 485, + 456, + 498 + ], + "spans": [ + { + "bbox": [ + 105, + 485, + 456, + 498 + ], + "score": 1.0, + "content": "trained with past checkpoints (in that case, to avoid strategy cycling) [7, 27, 28, 39, 69].", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 22.5, + "bbox_fs": [ + 104, + 431, + 506, + 498 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 510, + 230, + 521 + ], + "lines": [ + { + "bbox": [ + 106, + 509, + 230, + 522 + ], + "spans": [ + { + "bbox": [ + 106, + 509, + 230, + 522 + ], + "score": 1.0, + "content": "2.2 Baselines and ablations", + "type": "text" + } + ], + "index": 26 + } + ], + "index": 26 + }, + { + "type": "text", + "bbox": [ + 108, + 529, + 505, + 552 + ], + "lines": [ + { + "bbox": [ + 106, + 529, + 505, + 542 + ], + "spans": [ + { + "bbox": [ + 106, + 529, + 505, + 542 + ], + "score": 1.0, + "content": "We compare FCP agents to the three baseline training methods listed below, each varying only in", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 106, + 540, + 493, + 553 + ], + "spans": [ + { + "bbox": [ + 106, + 540, + 493, + 553 + ], + "score": 1.0, + "content": "their set of training partners, with the RL algorithm and architecture consistent across all agents:", + "type": "text" + } + ], + "index": 28 + } + ], + "index": 27.5, + "bbox_fs": [ + 106, + 529, + 505, + 553 + ] + }, + { + "type": "index", + "bbox": [ + 129, + 560, + 506, + 601 + ], + "lines": [ + { + "bbox": [ + 129, + 560, + 455, + 573 + ], + "spans": [ + { + "bbox": [ + 129, + 560, + 455, + 573 + ], + "score": 1.0, + "content": "1. Self-play (SP), where agents learn solely through interaction with themselves.", + "type": "text" + } + ], + "index": 29, + "is_list_start_line": true + }, + { + "bbox": [ + 128, + 573, + 507, + 588 + ], + "spans": [ + { + "bbox": [ + 128, + 573, + 507, + 588 + ], + "score": 1.0, + "content": "2. Population-play (PP), where a population of agents are co-trained through random pairings.", + "type": "text" + } + ], + "index": 30, + "is_list_start_line": true + }, + { + "bbox": [ + 128, + 587, + 506, + 602 + ], + "spans": [ + { + "bbox": [ + 128, + 587, + 506, + 602 + ], + "score": 1.0, + "content": "3. Behavioral cloning play (BCP), where an agent is trained with a BC model of a human [12].", + "type": "text" + } + ], + "index": 31, + "is_list_start_line": true + } + ], + "index": 30, + "bbox_fs": [ + 128, + 560, + 507, + 602 + ] + }, + { + "type": "text", + "bbox": [ + 110, + 608, + 473, + 620 + ], + "lines": [ + { + "bbox": [ + 107, + 607, + 474, + 621 + ], + "spans": [ + { + "bbox": [ + 107, + 607, + 474, + 621 + ], + "score": 1.0, + "content": "We also evaluate three variations on FCP to better understand the conditions for its success:", + "type": "text" + } + ], + "index": 32 + } + ], + "index": 32, + "bbox_fs": [ + 107, + 607, + 474, + 621 + ] + }, + { + "type": "text", + "bbox": [ + 130, + 627, + 505, + 723 + ], + "lines": [ + { + "bbox": [ + 129, + 627, + 505, + 641 + ], + "spans": [ + { + "bbox": [ + 129, + 627, + 505, + 641 + ], + "score": 1.0, + "content": "1. To test the importance of including past checkpoints in training, we evaluate an ablation", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 141, + 639, + 505, + 651 + ], + "spans": [ + { + "bbox": [ + 141, + 639, + 505, + 651 + ], + "score": 1.0, + "content": "of FCP in which agents are trained only with the converged checkpoints of their partners", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 142, + 650, + 273, + 662 + ], + "spans": [ + { + "bbox": [ + 142, + 650, + 145, + 661 + ], + "score": 0.0, + "content": "", + "type": "text" + }, + { + "bbox": [ + 145, + 650, + 177, + 662 + ], + "score": 0.84, + "content": "\\mathrm { F C P } _ { - T }", + "type": "inline_equation" + }, + { + "bbox": [ + 177, + 650, + 273, + 661 + ], + "score": 1.0, + "content": "for “FCP minus time”).", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 129, + 664, + 506, + 677 + ], + "spans": [ + { + "bbox": [ + 129, + 664, + 506, + 677 + ], + "score": 1.0, + "content": "2. To test whether FCP would benefit from additional diversity in its partner population, we", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 142, + 676, + 505, + 687 + ], + "spans": [ + { + "bbox": [ + 142, + 676, + 505, + 687 + ], + "score": 1.0, + "content": "evaluate an augmentation of FCP in which the population of SP partners varies not just in", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 141, + 686, + 485, + 698 + ], + "spans": [ + { + "bbox": [ + 141, + 686, + 294, + 697 + ], + "score": 1.0, + "content": "random seed, but also in architecture", + "type": "text" + }, + { + "bbox": [ + 295, + 686, + 326, + 698 + ], + "score": 0.88, + "content": "\\operatorname { F C P } _ { + A }", + "type": "inline_equation" + }, + { + "bbox": [ + 326, + 686, + 485, + 697 + ], + "score": 1.0, + "content": "for “FCP plus architectural variation”).", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 128, + 699, + 505, + 713 + ], + "spans": [ + { + "bbox": [ + 128, + 699, + 505, + 713 + ], + "score": 1.0, + "content": "3. To test whether architectural variation can serve as a full replacement for playing with past", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 141, + 710, + 455, + 724 + ], + "spans": [ + { + "bbox": [ + 141, + 710, + 399, + 724 + ], + "score": 1.0, + "content": "checkpoints, we evaluate the combination of both modifications", + "type": "text" + }, + { + "bbox": [ + 399, + 711, + 450, + 723 + ], + "score": 0.87, + "content": "( \\mathrm { F C P } _ { - T , + A } )", + "type": "inline_equation" + }, + { + "bbox": [ + 451, + 710, + 455, + 724 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 36.5, + "bbox_fs": [ + 128, + 627, + 506, + 724 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "title", + "bbox": [ + 107, + 73, + 186, + 84 + ], + "lines": [ + { + "bbox": [ + 105, + 71, + 188, + 86 + ], + "spans": [ + { + "bbox": [ + 105, + 71, + 188, + 86 + ], + "score": 1.0, + "content": "2.3 Environment", + "type": "text" + } + ], + "index": 0 + } + ], + "index": 0 + }, + { + "type": "text", + "bbox": [ + 107, + 93, + 505, + 127 + ], + "lines": [ + { + "bbox": [ + 105, + 92, + 505, + 105 + ], + "spans": [ + { + "bbox": [ + 105, + 92, + 505, + 105 + ], + "score": 1.0, + "content": "Following prior work on zero-shot coordination in human-agent interaction, we study the Overcooked", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 103, + 506, + 117 + ], + "spans": [ + { + "bbox": [ + 105, + 103, + 506, + 117 + ], + "score": 1.0, + "content": "environment (see Figure 3) [12, 13, 38, 50, 70]. We draw particular inspiration from the environment", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 114, + 322, + 127 + ], + "spans": [ + { + "bbox": [ + 105, + 114, + 322, + 127 + ], + "score": 1.0, + "content": "in Carroll et al. [12]. For full details, see Appendix A.", + "type": "text" + } + ], + "index": 3 + } + ], + "index": 2 + }, + { + "type": "text", + "bbox": [ + 106, + 131, + 505, + 186 + ], + "lines": [ + { + "bbox": [ + 105, + 130, + 506, + 144 + ], + "spans": [ + { + "bbox": [ + 105, + 130, + 506, + 144 + ], + "score": 1.0, + "content": "In this environment, players are placed into a gridworld kitchen as chefs and tasked with delivering as", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 141, + 504, + 153 + ], + "spans": [ + { + "bbox": [ + 106, + 141, + 504, + 153 + ], + "score": 1.0, + "content": "many cooked dishes of tomato soup as possible within an episode. This involves a series of sequential", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 106, + 153, + 505, + 165 + ], + "spans": [ + { + "bbox": [ + 106, + 153, + 505, + 165 + ], + "score": 1.0, + "content": "high-level actions to which both players can contribute: collecting tomatoes, depositing them into", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 161, + 505, + 179 + ], + "spans": [ + { + "bbox": [ + 105, + 161, + 505, + 179 + ], + "score": 1.0, + "content": "cooking pots, letting the tomatoes cook into soup, collecting a dish, getting the soup, and delivering", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 174, + 368, + 188 + ], + "spans": [ + { + "bbox": [ + 105, + 174, + 368, + 188 + ], + "score": 1.0, + "content": "it. 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Highlighted in bold are the terms used to refer to each in the rest of this paper.", + "type": "text" + } + ], + "index": 24 + } + ], + "index": 23.5 + } + ], + "index": 22.25 + }, + { + "type": "title", + "bbox": [ + 107, + 570, + 229, + 582 + ], + "lines": [ + { + "bbox": [ + 106, + 569, + 230, + 584 + ], + "spans": [ + { + "bbox": [ + 106, + 569, + 230, + 584 + ], + "score": 1.0, + "content": "2.4 Implementation details", + "type": "text" + } + ], + "index": 25 + } + ], + "index": 25 + }, + { + "type": "text", + "bbox": [ + 107, + 591, + 505, + 613 + ], + "lines": [ + { + "bbox": [ + 106, + 591, + 507, + 603 + ], + "spans": [ + { + "bbox": [ + 106, + 591, + 507, + 603 + ], + "score": 1.0, + "content": "Here we highlight several key implementation details for our training methods. For full details,", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 601, + 460, + 614 + ], + "spans": [ + { + "bbox": [ + 105, + 601, + 460, + 614 + ], + "score": 1.0, + "content": "including the architectures, hyperparameters, and compute used, please see Appendix B.", + "type": "text" + } + ], + "index": 27 + } + ], + "index": 26.5 + }, + { + "type": "text", + "bbox": [ + 107, + 617, + 505, + 662 + ], + "lines": [ + { + "bbox": [ + 105, + 617, + 504, + 630 + ], + "spans": [ + { + "bbox": [ + 105, + 617, + 341, + 630 + ], + "score": 1.0, + "content": "For our reinforcement learning agents, we use the V-MPO", + "type": "text" + }, + { + "bbox": [ + 342, + 617, + 359, + 629 + ], + "score": 0.45, + "content": "\\pmb { \\Vert 6 5 \\Vert }", + "type": "inline_equation" + }, + { + "bbox": [ + 360, + 617, + 504, + 630 + ], + "score": 1.0, + "content": "algorithm along with a ResNet [26]", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 628, + 505, + 641 + ], + "spans": [ + { + "bbox": [ + 105, + 628, + 155, + 641 + ], + "score": 1.0, + "content": "plus LSTM", + "type": "text" + }, + { + "bbox": [ + 155, + 628, + 173, + 640 + ], + "score": 0.7, + "content": "\\mathbb { \\left| \\bigstar \\bigstar \\right\\| }", + "type": "inline_equation" + }, + { + "bbox": [ + 173, + 628, + 505, + 641 + ], + "score": 1.0, + "content": "architecture which we found led to optimal behavior across all layouts. 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We draw particular inspiration from the environment", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 114, + 322, + 127 + ], + "spans": [ + { + "bbox": [ + 105, + 114, + 322, + 127 + ], + "score": 1.0, + "content": "in Carroll et al. [12]. For full details, see Appendix A.", + "type": "text" + } + ], + "index": 3 + } + ], + "index": 2, + "bbox_fs": [ + 105, + 92, + 506, + 127 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 131, + 505, + 186 + ], + "lines": [ + { + "bbox": [ + 105, + 130, + 506, + 144 + ], + "spans": [ + { + "bbox": [ + 105, + 130, + 506, + 144 + ], + "score": 1.0, + "content": "In this environment, players are placed into a gridworld kitchen as chefs and tasked with delivering as", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 106, + 141, + 504, + 153 + ], + "spans": [ + { + "bbox": [ + 106, + 141, + 504, + 153 + ], + "score": 1.0, + "content": "many cooked dishes of tomato soup as possible within an episode. This involves a series of sequential", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 106, + 153, + 505, + 165 + ], + "spans": [ + { + "bbox": [ + 106, + 153, + 505, + 165 + ], + "score": 1.0, + "content": "high-level actions to which both players can contribute: collecting tomatoes, depositing them into", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 161, + 505, + 179 + ], + "spans": [ + { + "bbox": [ + 105, + 161, + 505, + 179 + ], + "score": 1.0, + "content": "cooking pots, letting the tomatoes cook into soup, collecting a dish, getting the soup, and delivering", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 174, + 368, + 188 + ], + "spans": [ + { + "bbox": [ + 105, + 174, + 368, + 188 + ], + "score": 1.0, + "content": "it. 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The behavior ofLÈ¡áyҏįŘį\u000eºµò‹µÒ¡y®", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 250, + 476, + 263 + ], + "spans": [ + { + "bbox": [ + 105, + 250, + 476, + 263 + ], + "score": 1.0, + "content": "interact varies based on the cell which the player is facing (e.g. place tomato on counter).", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 13, + "bbox_fs": [ + 105, + 228, + 506, + 263 + ] + }, + { + "type": "image", + "bbox": [ + 118, + 273, + 492, + 419 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 118, + 273, + 492, + 419 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 118, + 273, + 492, + 419 + ], + "spans": [ + { + "bbox": [ + 118, + 273, + 492, + 419 + ], + "score": 0.969, + "type": "image", + "image_path": "48e8af75423e0fa5e5c23e2088c49716c4c6044ee3579f26cdd9911dbb4b4925.jpg" + } + ] + } + ], + "index": 16, + "virtual_lines": [ + { + "bbox": [ + 118, + 273, + 492, + 321.6666666666667 + ], + "spans": [], + "index": 15 + }, + { + "bbox": [ + 118, + 321.6666666666667, + 492, + 370.33333333333337 + ], + "spans": [], + "index": 16 + }, + { + "bbox": [ + 118, + 370.33333333333337, + 492, + 419.00000000000006 + ], + "spans": [], + "index": 17 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 107, + 426, + 503, + 448 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 425, + 504, + 438 + ], + "spans": [ + { + "bbox": [ + 106, + 425, + 504, + 438 + ], + "score": 1.0, + "content": "Figure 3: The Overcooked environment: a two-player common-payoff game in which players must", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 436, + 254, + 450 + ], + "spans": [ + { + "bbox": [ + 105, + 436, + 254, + 450 + ], + "score": 1.0, + "content": "coordinate to cook and deliver soup.", + "type": "text" + } + ], + "index": 19 + } + ], + "index": 18.5 + } + ], + "index": 17.25 + }, + { + "type": "image", + "bbox": [ + 120, + 459, + 492, + 521 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 120, + 459, + 492, + 521 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 120, + 459, + 492, + 521 + ], + "spans": [ + { + "bbox": [ + 120, + 459, + 492, + 521 + ], + "score": 0.963, + "type": "image", + "image_path": "2b3253fcaac0677dbb15cc1ed5aa0663e561ff52605ba15bd61605f96c716872.jpg" + } + ] + } + ], + "index": 21, + "virtual_lines": [ + { + "bbox": [ + 120, + 459, + 492, + 479.6666666666667 + ], + "spans": [], + "index": 20 + }, + { + "bbox": [ + 120, + 479.6666666666667, + 492, + 500.33333333333337 + ], + "spans": [], + "index": 21 + }, + { + "bbox": [ + 120, + 500.33333333333337, + 492, + 521.0 + ], + "spans": [], + "index": 22 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 104, + 527, + 504, + 550 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 106, + 526, + 505, + 540 + ], + "spans": [ + { + "bbox": [ + 106, + 526, + 505, + 540 + ], + "score": 1.0, + "content": "Figure 4: Layouts: the kitchens which agents and humans play in, each emphasizing different", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 537, + 505, + 551 + ], + "spans": [ + { + "bbox": [ + 105, + 537, + 505, + 551 + ], + "score": 1.0, + "content": "coordination strategies. 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When varying architecture for the training partners of the", + "type": "text" + }, + { + "bbox": [ + 456, + 711, + 487, + 722 + ], + "score": 0.9, + "content": "\\operatorname { F C P } _ { + A }", + "type": "inline_equation" + }, + { + "bbox": [ + 487, + 711, + 505, + 723 + ], + "score": 1.0, + "content": "and", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 106, + 72, + 505, + 86 + ], + "spans": [ + { + "bbox": [ + 106, + 73, + 152, + 85 + ], + "score": 0.85, + "content": "\\mathrm { F C P } _ { - T , + A }", + "type": "inline_equation", + "cross_page": true + }, + { + "bbox": [ + 153, + 72, + 505, + 86 + ], + "score": 1.0, + "content": "variants, we vary whether the partners use memory (i.e. LSTM vs not) and the width", + "type": "text", + "cross_page": true + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 83, + 505, + 95 + ], + "spans": [ + { + "bbox": [ + 105, + 83, + 505, + 95 + ], + "score": 1.0, + "content": "of their policy and value networks (i.e. 16 vs 256). 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In total, we train 8 agents for each of the 4", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 95, + 499, + 107 + ], + "spans": [ + { + "bbox": [ + 105, + 95, + 306, + 107 + ], + "score": 1.0, + "content": "combinations, leaving the total population size of", + "type": "text" + }, + { + "bbox": [ + 306, + 95, + 339, + 105 + ], + "score": 0.89, + "content": "N = 3 2", + "type": "inline_equation" + }, + { + "bbox": [ + 340, + 95, + 499, + 107 + ], + "score": 1.0, + "content": "unchanged, ensuring a fair comparison.", + "type": "text" + } + ], + "index": 2 + } + ], + "index": 1 + }, + { + "type": "text", + "bbox": [ + 107, + 110, + 505, + 188 + ], + "lines": [ + { + "bbox": [ + 105, + 110, + 505, + 124 + ], + "spans": [ + { + "bbox": [ + 105, + 110, + 257, + 124 + ], + "score": 1.0, + "content": "To train agents via behavioral cloning", + "type": "text" + }, + { + "bbox": [ + 257, + 110, + 274, + 122 + ], + "score": 0.79, + "content": "\\left[ \\left[ 5 8 \\right] \\right]", + "type": "inline_equation" + }, + { + "bbox": [ + 275, + 110, + 398, + 124 + ], + "score": 1.0, + "content": ", we use the open-source Acme", + "type": "text" + }, + { + "bbox": [ + 399, + 110, + 416, + 122 + ], + "score": 0.77, + "content": "\\pmb { \\mathbb { B } } 0 \\|", + "type": "inline_equation" + }, + { + "bbox": [ + 417, + 110, + 505, + 124 + ], + "score": 1.0, + "content": "to learn a policy from", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 122, + 505, + 134 + ], + "spans": [ + { + "bbox": [ + 105, + 122, + 505, + 134 + ], + "score": 1.0, + "content": "human gameplay data. Specifically, we collected 5 human-human trajectories of length 1200 time", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 132, + 506, + 145 + ], + "spans": [ + { + "bbox": [ + 105, + 132, + 506, + 145 + ], + "score": 1.0, + "content": "steps for each of the 5 layouts, resulting in 60k total environment steps. We divide this data in half", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 106, + 145, + 505, + 156 + ], + "spans": [ + { + "bbox": [ + 106, + 145, + 505, + 156 + ], + "score": 1.0, + "content": "and train two BC agents: (1) a partner for training a BCP agent, and (2) a “human proxy” partner for", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 154, + 505, + 167 + ], + "spans": [ + { + "bbox": [ + 105, + 154, + 298, + 167 + ], + "score": 1.0, + "content": "agent-agent evaluation. Following Carroll et al.", + "type": "text" + }, + { + "bbox": [ + 299, + 154, + 316, + 166 + ], + "score": 0.61, + "content": "\\mathbb { \\overline { { \\lVert \\lambda \\rVert } } }", + "type": "inline_equation" + }, + { + "bbox": [ + 317, + 154, + 505, + 167 + ], + "score": 1.0, + "content": ", we use a set of feature-based observations for", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 165, + 505, + 178 + ], + "spans": [ + { + "bbox": [ + 105, + 165, + 505, + 178 + ], + "score": 1.0, + "content": "the agents (as opposed to RGB) and generate comparable results: performance is higher on 3 layouts", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 177, + 452, + 189 + ], + "spans": [ + { + "bbox": [ + 105, + 177, + 452, + 189 + ], + "score": 1.0, + "content": "(asymmetric, cramped, and ring) but poorer on the other 2 (circuit and forced).", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 6 + }, + { + "type": "title", + "bbox": [ + 107, + 206, + 194, + 219 + ], + "lines": [ + { + "bbox": [ + 105, + 205, + 196, + 221 + ], + "spans": [ + { + "bbox": [ + 105, + 205, + 196, + 221 + ], + "score": 1.0, + "content": "3 Related work", + "type": "text" + } + ], + "index": 10 + } + ], + "index": 10 + }, + { + "type": "text", + "bbox": [ + 106, + 233, + 505, + 333 + ], + "lines": [ + { + "bbox": [ + 106, + 232, + 505, + 245 + ], + "spans": [ + { + "bbox": [ + 106, + 233, + 456, + 245 + ], + "score": 1.0, + "content": "Ad-hoc team play There is a large and diverse body of literature on ad-hoc team-play", + "type": "text" + }, + { + "bbox": [ + 456, + 232, + 483, + 245 + ], + "score": 0.73, + "content": " { \\mathbb { B } } , { \\mathbb { G } } 6 { \\mathbb { I } }", + "type": "inline_equation" + }, + { + "bbox": [ + 483, + 233, + 505, + 245 + ], + "score": 1.0, + "content": ", also", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 244, + 505, + 257 + ], + "spans": [ + { + "bbox": [ + 105, + 244, + 243, + 257 + ], + "score": 1.0, + "content": "known as zero-shot coordination", + "type": "text" + }, + { + "bbox": [ + 243, + 244, + 260, + 256 + ], + "score": 0.49, + "content": "[ \\overbrace { 3 \\mathrm { 1 } } ]", + "type": "inline_equation" + }, + { + "bbox": [ + 261, + 244, + 505, + 257 + ], + "score": 1.0, + "content": ". Prior work based in game-theoretic settings has suggested", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 255, + 505, + 267 + ], + "spans": [ + { + "bbox": [ + 106, + 255, + 205, + 267 + ], + "score": 1.0, + "content": "the benefits of planning", + "type": "text" + }, + { + "bbox": [ + 205, + 255, + 223, + 267 + ], + "score": 0.64, + "content": "\\mathbb { [ [ \\mathrm { { 7 1 } ] } }", + "type": "inline_equation" + }, + { + "bbox": [ + 223, + 255, + 291, + 267 + ], + "score": 1.0, + "content": ", online learning", + "type": "text" + }, + { + "bbox": [ + 291, + 255, + 309, + 267 + ], + "score": 0.35, + "content": "\\mathbb { \\left[ \\left. 5 1 \\right] \\right. }", + "type": "inline_equation" + }, + { + "bbox": [ + 309, + 255, + 428, + 267 + ], + "score": 1.0, + "content": ", and novel solution concepts", + "type": "text" + }, + { + "bbox": [ + 429, + 255, + 441, + 266 + ], + "score": 0.78, + "content": "\\bar { \\mathbb { D } }", + "type": "inline_equation" + }, + { + "bbox": [ + 442, + 255, + 505, + 267 + ], + "score": 1.0, + "content": ", to name a few", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 266, + 506, + 279 + ], + "spans": [ + { + "bbox": [ + 106, + 266, + 506, + 279 + ], + "score": 1.0, + "content": "examples. More recently, multi-agent deep reinforcement learning has provided the tools to scale", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 276, + 506, + 290 + ], + "spans": [ + { + "bbox": [ + 105, + 276, + 506, + 290 + ], + "score": 1.0, + "content": "to more complex gridworld or continuous control settings, leading to work on hierarchical social", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 286, + 505, + 302 + ], + "spans": [ + { + "bbox": [ + 105, + 286, + 144, + 302 + ], + "score": 1.0, + "content": "planning", + "type": "text" + }, + { + "bbox": [ + 144, + 288, + 162, + 299 + ], + "score": 0.76, + "content": "\\widehat { \\left\\| 3 6 \\right\\| }", + "type": "inline_equation" + }, + { + "bbox": [ + 162, + 286, + 324, + 302 + ], + "score": 1.0, + "content": ", adapting to existing social conventions", + "type": "text" + }, + { + "bbox": [ + 324, + 288, + 356, + 299 + ], + "score": 0.34, + "content": "\\checkmark", + "type": "inline_equation" + }, + { + "bbox": [ + 357, + 286, + 438, + 302 + ], + "score": 1.0, + "content": ", trajectory diversity", + "type": "text" + }, + { + "bbox": [ + 439, + 288, + 456, + 299 + ], + "score": 0.6, + "content": "\\lVert \\boldsymbol { \\mathsf { A } } \\boldsymbol { \\mathsf { S } } \\rVert", + "type": "inline_equation" + }, + { + "bbox": [ + 457, + 286, + 505, + 302 + ], + "score": 1.0, + "content": ", and theory", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 298, + 505, + 311 + ], + "spans": [ + { + "bbox": [ + 105, + 298, + 505, + 311 + ], + "score": 1.0, + "content": "of mind [14]. Ad-hoc team-play among novel agent partners is also an object of active study in the", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 309, + 505, + 322 + ], + "spans": [ + { + "bbox": [ + 105, + 309, + 505, + 322 + ], + "score": 1.0, + "content": "emergent communication literature [10, 11, 43]. This prior work has tended to focus on generalization", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 106, + 321, + 345, + 333 + ], + "spans": [ + { + "bbox": [ + 106, + 321, + 345, + 333 + ], + "score": 1.0, + "content": "to held-out agent partners as a proxy for human co-players.", + "type": "text" + } + ], + "index": 19 + } + ], + "index": 15 + }, + { + "type": "text", + "bbox": [ + 107, + 337, + 505, + 381 + ], + "lines": [ + { + "bbox": [ + 105, + 335, + 505, + 350 + ], + "spans": [ + { + "bbox": [ + 105, + 335, + 505, + 350 + ], + "score": 1.0, + "content": "Collaborative play with novel humans has been evaluated more actively in the context of training", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 104, + 347, + 507, + 361 + ], + "spans": [ + { + "bbox": [ + 104, + 347, + 507, + 361 + ], + "score": 1.0, + "content": "agent assistants; see for instance [57, 68]. To our knowledge, our FCP agents represent the state-", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 358, + 505, + 371 + ], + "spans": [ + { + "bbox": [ + 105, + 358, + 505, + 371 + ], + "score": 1.0, + "content": "of-the-art in coordinating with novel human partners on an equal footing of capabilities in a rich", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 370, + 426, + 381 + ], + "spans": [ + { + "bbox": [ + 105, + 370, + 426, + 381 + ], + "score": 1.0, + "content": "gridworld environment, as measured by the challenge tasks in Carroll et al. [12].", + "type": "text" + } + ], + "index": 23 + } + ], + "index": 21.5 + }, + { + "type": "text", + "bbox": [ + 107, + 386, + 505, + 464 + ], + "lines": [ + { + "bbox": [ + 105, + 384, + 505, + 399 + ], + "spans": [ + { + "bbox": [ + 105, + 384, + 505, + 399 + ], + "score": 1.0, + "content": "Diversity in multi-agent reinforcement learning In multi-agent reinforcement learning, agents", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 397, + 505, + 410 + ], + "spans": [ + { + "bbox": [ + 105, + 397, + 505, + 410 + ], + "score": 1.0, + "content": "that train with behaviorally diverse populations of game partners tend to demonstrate stronger", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 106, + 407, + 506, + 421 + ], + "spans": [ + { + "bbox": [ + 106, + 407, + 506, + 421 + ], + "score": 1.0, + "content": "performance than their self-play counterparts. For example, across a range of multi-agent games,", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 419, + 505, + 432 + ], + "spans": [ + { + "bbox": [ + 105, + 419, + 505, + 432 + ], + "score": 1.0, + "content": "generalization to held-out populations can be improved by training larger and more diverse populations", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 106, + 429, + 505, + 443 + ], + "spans": [ + { + "bbox": [ + 106, + 429, + 505, + 443 + ], + "score": 1.0, + "content": "[13, 42, 50]. In mixed-motive settings, cooperation among agents can be encouraged through social", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 441, + 505, + 453 + ], + "spans": [ + { + "bbox": [ + 106, + 441, + 505, + 453 + ], + "score": 1.0, + "content": "diversity, such as in player preferences and rewards [3, 47, 49]. Similarly, competitiveness can be", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 451, + 472, + 464 + ], + "spans": [ + { + "bbox": [ + 105, + 451, + 422, + 464 + ], + "score": 1.0, + "content": "optimized through selective matchmaking between increasingly diverse agents", + "type": "text" + }, + { + "bbox": [ + 423, + 451, + 469, + 463 + ], + "score": 0.6, + "content": "\\pm \\boxed { 1 2 4 } \\boxed { 3 9 } \\boxed { 6 9 }", + "type": "inline_equation" + }, + { + "bbox": [ + 470, + 451, + 472, + 464 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 30 + } + ], + "index": 27 + }, + { + "type": "text", + "bbox": [ + 107, + 468, + 505, + 512 + ], + "lines": [ + { + "bbox": [ + 105, + 468, + 505, + 480 + ], + "spans": [ + { + "bbox": [ + 105, + 468, + 505, + 480 + ], + "score": 1.0, + "content": "Despite the increased focus on improving multi-agent performance, evaluation has typically been", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 479, + 505, + 492 + ], + "spans": [ + { + "bbox": [ + 105, + 479, + 505, + 492 + ], + "score": 1.0, + "content": "constrained to agent-agent settings. High-performing agents have infrequently been evaluated with", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 489, + 505, + 504 + ], + "spans": [ + { + "bbox": [ + 105, + 489, + 308, + 504 + ], + "score": 1.0, + "content": "humans, particularly in non-competitive domains", + "type": "text" + }, + { + "bbox": [ + 308, + 489, + 326, + 501 + ], + "score": 0.66, + "content": "\\dot { \\left. \\overline { { \\dot { \\left. \\dot { \\theta } \\right\\| } } } \\right. }", + "type": "inline_equation" + }, + { + "bbox": [ + 326, + 489, + 505, + 504 + ], + "score": 1.0, + "content": ". We add to this growing literature, showing", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 501, + 471, + 513 + ], + "spans": [ + { + "bbox": [ + 105, + 501, + 471, + 513 + ], + "score": 1.0, + "content": "that training with diversity is a powerful approach for effective human-agent collaboration.", + "type": "text" + } + ], + "index": 34 + } + ], + "index": 32.5 + }, + { + "type": "text", + "bbox": [ + 106, + 517, + 505, + 595 + ], + "lines": [ + { + "bbox": [ + 105, + 515, + 506, + 531 + ], + "spans": [ + { + "bbox": [ + 105, + 515, + 506, + 531 + ], + "score": 1.0, + "content": "Human-agent interaction In recent years, increased attention has been directed toward designing", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 527, + 506, + 541 + ], + "spans": [ + { + "bbox": [ + 105, + 527, + 465, + 541 + ], + "score": 1.0, + "content": "machine learning agents capable of collaborating with humans [41, 57, 68, 72] (see also", + "type": "text" + }, + { + "bbox": [ + 465, + 528, + 482, + 540 + ], + "score": 0.76, + "content": "\\textcircled { 1 1 6 } \\textcircled { }", + "type": "inline_equation" + }, + { + "bbox": [ + 483, + 527, + 506, + 541 + ], + "score": 1.0, + "content": "for a", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 537, + 505, + 552 + ], + "spans": [ + { + "bbox": [ + 105, + 537, + 304, + 552 + ], + "score": 1.0, + "content": "broader review on Cooperative AI). Tylkin et al.", + "type": "text" + }, + { + "bbox": [ + 305, + 539, + 322, + 550 + ], + "score": 0.64, + "content": "\\lVert \\overline { { 6 8 } } \\rVert", + "type": "inline_equation" + }, + { + "bbox": [ + 323, + 537, + 505, + 552 + ], + "score": 1.0, + "content": "is particularly notable in also demonstrating", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 550, + 505, + 562 + ], + "spans": [ + { + "bbox": [ + 106, + 550, + 505, + 562 + ], + "score": 1.0, + "content": "that partially trained agents can be useful learning targets for human helpers, although in a different", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 106, + 561, + 505, + 573 + ], + "spans": [ + { + "bbox": [ + 106, + 561, + 505, + 573 + ], + "score": 1.0, + "content": "domain (cooperative Atari). Our method, FCP, can be seen as extending theirs by training with", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 572, + 506, + 583 + ], + "spans": [ + { + "bbox": [ + 105, + 572, + 506, + 583 + ], + "score": 1.0, + "content": "multiple “skill levels” and random seeds, rather than just one, which we demonstrate to be crucial to", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 583, + 331, + 595 + ], + "spans": [ + { + "bbox": [ + 105, + 583, + 331, + 595 + ], + "score": 1.0, + "content": "our agents’ performance (Tables 1 and 2 and Figure 7b).", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 38 + }, + { + "type": "text", + "bbox": [ + 107, + 599, + 505, + 666 + ], + "lines": [ + { + "bbox": [ + 106, + 599, + 506, + 612 + ], + "spans": [ + { + "bbox": [ + 106, + 599, + 343, + 612 + ], + "score": 1.0, + "content": "A key preceding entry in this research area is Carroll et al.", + "type": "text" + }, + { + "bbox": [ + 343, + 599, + 361, + 610 + ], + "score": 0.58, + "content": "[ \\mathbb { 1 2 } ]", + "type": "inline_equation" + }, + { + "bbox": [ + 361, + 599, + 506, + 612 + ], + "score": 1.0, + "content": ", who similarly investigated human-", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 609, + 507, + 623 + ], + "spans": [ + { + "bbox": [ + 105, + 609, + 507, + 623 + ], + "score": 1.0, + "content": "agent coordination in Overcooked. We use their method (BCP) as a baseline throughout our exper-", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 620, + 506, + 633 + ], + "spans": [ + { + "bbox": [ + 105, + 621, + 172, + 633 + ], + "score": 1.0, + "content": "iments (Section", + "type": "text" + }, + { + "bbox": [ + 172, + 620, + 191, + 633 + ], + "score": 0.44, + "content": "\\boxed { 2 . 2 }", + "type": "inline_equation" + }, + { + "bbox": [ + 191, + 621, + 506, + 633 + ], + "score": 1.0, + "content": ". Relative to BCP, our approach removes the need for the expensive step of", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 632, + 506, + 644 + ], + "spans": [ + { + "bbox": [ + 106, + 632, + 506, + 644 + ], + "score": 1.0, + "content": "human data collection for agent training. Furthermore, through our novel human-agent experimental", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 642, + 506, + 655 + ], + "spans": [ + { + "bbox": [ + 106, + 642, + 506, + 655 + ], + "score": 1.0, + "content": "design, we go beyond objective performance metrics to compare the subjective preferences that", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 654, + 441, + 666 + ], + "spans": [ + { + "bbox": [ + 105, + 654, + 441, + 666 + ], + "score": 1.0, + "content": "agents generate. For a detailed comparison of methods and results, see Appendix E.", + "type": "text" + } + ], + "index": 47 + } + ], + "index": 44.5 + }, + { + "type": "title", + "bbox": [ + 107, + 684, + 305, + 697 + ], + "lines": [ + { + "bbox": [ + 104, + 682, + 306, + 700 + ], + "spans": [ + { + "bbox": [ + 104, + 682, + 306, + 700 + ], + "score": 1.0, + "content": "4 Zero-shot coordination with agents", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 48 + }, + { + "type": "text", + "bbox": [ + 104, + 710, + 487, + 722 + ], + "lines": [ + { + "bbox": [ + 105, + 709, + 489, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 709, + 489, + 723 + ], + "score": 1.0, + "content": "In this section, we evaluate our FCP agent, its ablations, and the baselines with held-out agents.", + "type": "text" + } + ], + "index": 49 + } + ], + "index": 49 + } + ], + "page_idx": 4, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 302, + 741, + 309, + 750 + ], + "lines": [ + { + "bbox": [ + 301, + 740, + 310, + 753 + ], + "spans": [ + { + "bbox": [ + 301, + 740, + 310, + 753 + ], + "score": 1.0, + "content": "5", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "text", + "bbox": [ + 107, + 73, + 505, + 106 + ], + "lines": [], + "index": 1, + "bbox_fs": [ + 105, + 72, + 505, + 107 + ], + "lines_deleted": true + }, + { + "type": "text", + "bbox": [ + 107, + 110, + 505, + 188 + ], + "lines": [ + { + "bbox": [ + 105, + 110, + 505, + 124 + ], + "spans": [ + { + "bbox": [ + 105, + 110, + 257, + 124 + ], + "score": 1.0, + "content": "To train agents via behavioral cloning", + "type": "text" + }, + { + "bbox": [ + 257, + 110, + 274, + 122 + ], + "score": 0.79, + "content": "\\left[ \\left[ 5 8 \\right] \\right]", + "type": "inline_equation" + }, + { + "bbox": [ + 275, + 110, + 398, + 124 + ], + "score": 1.0, + "content": ", we use the open-source Acme", + "type": "text" + }, + { + "bbox": [ + 399, + 110, + 416, + 122 + ], + "score": 0.77, + "content": "\\pmb { \\mathbb { B } } 0 \\|", + "type": "inline_equation" + }, + { + "bbox": [ + 417, + 110, + 505, + 124 + ], + "score": 1.0, + "content": "to learn a policy from", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 122, + 505, + 134 + ], + "spans": [ + { + "bbox": [ + 105, + 122, + 505, + 134 + ], + "score": 1.0, + "content": "human gameplay data. Specifically, we collected 5 human-human trajectories of length 1200 time", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 132, + 506, + 145 + ], + "spans": [ + { + "bbox": [ + 105, + 132, + 506, + 145 + ], + "score": 1.0, + "content": "steps for each of the 5 layouts, resulting in 60k total environment steps. We divide this data in half", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 106, + 145, + 505, + 156 + ], + "spans": [ + { + "bbox": [ + 106, + 145, + 505, + 156 + ], + "score": 1.0, + "content": "and train two BC agents: (1) a partner for training a BCP agent, and (2) a “human proxy” partner for", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 154, + 505, + 167 + ], + "spans": [ + { + "bbox": [ + 105, + 154, + 298, + 167 + ], + "score": 1.0, + "content": "agent-agent evaluation. Following Carroll et al.", + "type": "text" + }, + { + "bbox": [ + 299, + 154, + 316, + 166 + ], + "score": 0.61, + "content": "\\mathbb { \\overline { { \\lVert \\lambda \\rVert } } }", + "type": "inline_equation" + }, + { + "bbox": [ + 317, + 154, + 505, + 167 + ], + "score": 1.0, + "content": ", we use a set of feature-based observations for", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 165, + 505, + 178 + ], + "spans": [ + { + "bbox": [ + 105, + 165, + 505, + 178 + ], + "score": 1.0, + "content": "the agents (as opposed to RGB) and generate comparable results: performance is higher on 3 layouts", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 177, + 452, + 189 + ], + "spans": [ + { + "bbox": [ + 105, + 177, + 452, + 189 + ], + "score": 1.0, + "content": "(asymmetric, cramped, and ring) but poorer on the other 2 (circuit and forced).", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 6, + "bbox_fs": [ + 105, + 110, + 506, + 189 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 206, + 194, + 219 + ], + "lines": [ + { + "bbox": [ + 105, + 205, + 196, + 221 + ], + "spans": [ + { + "bbox": [ + 105, + 205, + 196, + 221 + ], + "score": 1.0, + "content": "3 Related work", + "type": "text" + } + ], + "index": 10 + } + ], + "index": 10 + }, + { + "type": "text", + "bbox": [ + 106, + 233, + 505, + 333 + ], + "lines": [ + { + "bbox": [ + 106, + 232, + 505, + 245 + ], + "spans": [ + { + "bbox": [ + 106, + 233, + 456, + 245 + ], + "score": 1.0, + "content": "Ad-hoc team play There is a large and diverse body of literature on ad-hoc team-play", + "type": "text" + }, + { + "bbox": [ + 456, + 232, + 483, + 245 + ], + "score": 0.73, + "content": " { \\mathbb { B } } , { \\mathbb { G } } 6 { \\mathbb { I } }", + "type": "inline_equation" + }, + { + "bbox": [ + 483, + 233, + 505, + 245 + ], + "score": 1.0, + "content": ", also", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 244, + 505, + 257 + ], + "spans": [ + { + "bbox": [ + 105, + 244, + 243, + 257 + ], + "score": 1.0, + "content": "known as zero-shot coordination", + "type": "text" + }, + { + "bbox": [ + 243, + 244, + 260, + 256 + ], + "score": 0.49, + "content": "[ \\overbrace { 3 \\mathrm { 1 } } ]", + "type": "inline_equation" + }, + { + "bbox": [ + 261, + 244, + 505, + 257 + ], + "score": 1.0, + "content": ". Prior work based in game-theoretic settings has suggested", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 106, + 255, + 505, + 267 + ], + "spans": [ + { + "bbox": [ + 106, + 255, + 205, + 267 + ], + "score": 1.0, + "content": "the benefits of planning", + "type": "text" + }, + { + "bbox": [ + 205, + 255, + 223, + 267 + ], + "score": 0.64, + "content": "\\mathbb { [ [ \\mathrm { { 7 1 } ] } }", + "type": "inline_equation" + }, + { + "bbox": [ + 223, + 255, + 291, + 267 + ], + "score": 1.0, + "content": ", online learning", + "type": "text" + }, + { + "bbox": [ + 291, + 255, + 309, + 267 + ], + "score": 0.35, + "content": "\\mathbb { \\left[ \\left. 5 1 \\right] \\right. }", + "type": "inline_equation" + }, + { + "bbox": [ + 309, + 255, + 428, + 267 + ], + "score": 1.0, + "content": ", and novel solution concepts", + "type": "text" + }, + { + "bbox": [ + 429, + 255, + 441, + 266 + ], + "score": 0.78, + "content": "\\bar { \\mathbb { D } }", + "type": "inline_equation" + }, + { + "bbox": [ + 442, + 255, + 505, + 267 + ], + "score": 1.0, + "content": ", to name a few", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 106, + 266, + 506, + 279 + ], + "spans": [ + { + "bbox": [ + 106, + 266, + 506, + 279 + ], + "score": 1.0, + "content": "examples. More recently, multi-agent deep reinforcement learning has provided the tools to scale", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 276, + 506, + 290 + ], + "spans": [ + { + "bbox": [ + 105, + 276, + 506, + 290 + ], + "score": 1.0, + "content": "to more complex gridworld or continuous control settings, leading to work on hierarchical social", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 286, + 505, + 302 + ], + "spans": [ + { + "bbox": [ + 105, + 286, + 144, + 302 + ], + "score": 1.0, + "content": "planning", + "type": "text" + }, + { + "bbox": [ + 144, + 288, + 162, + 299 + ], + "score": 0.76, + "content": "\\widehat { \\left\\| 3 6 \\right\\| }", + "type": "inline_equation" + }, + { + "bbox": [ + 162, + 286, + 324, + 302 + ], + "score": 1.0, + "content": ", adapting to existing social conventions", + "type": "text" + }, + { + "bbox": [ + 324, + 288, + 356, + 299 + ], + "score": 0.34, + "content": "\\checkmark", + "type": "inline_equation" + }, + { + "bbox": [ + 357, + 286, + 438, + 302 + ], + "score": 1.0, + "content": ", trajectory diversity", + "type": "text" + }, + { + "bbox": [ + 439, + 288, + 456, + 299 + ], + "score": 0.6, + "content": "\\lVert \\boldsymbol { \\mathsf { A } } \\boldsymbol { \\mathsf { S } } \\rVert", + "type": "inline_equation" + }, + { + "bbox": [ + 457, + 286, + 505, + 302 + ], + "score": 1.0, + "content": ", and theory", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 298, + 505, + 311 + ], + "spans": [ + { + "bbox": [ + 105, + 298, + 505, + 311 + ], + "score": 1.0, + "content": "of mind [14]. Ad-hoc team-play among novel agent partners is also an object of active study in the", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 309, + 505, + 322 + ], + "spans": [ + { + "bbox": [ + 105, + 309, + 505, + 322 + ], + "score": 1.0, + "content": "emergent communication literature [10, 11, 43]. This prior work has tended to focus on generalization", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 106, + 321, + 345, + 333 + ], + "spans": [ + { + "bbox": [ + 106, + 321, + 345, + 333 + ], + "score": 1.0, + "content": "to held-out agent partners as a proxy for human co-players.", + "type": "text" + } + ], + "index": 19 + } + ], + "index": 15, + "bbox_fs": [ + 105, + 232, + 506, + 333 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 337, + 505, + 381 + ], + "lines": [ + { + "bbox": [ + 105, + 335, + 505, + 350 + ], + "spans": [ + { + "bbox": [ + 105, + 335, + 505, + 350 + ], + "score": 1.0, + "content": "Collaborative play with novel humans has been evaluated more actively in the context of training", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 104, + 347, + 507, + 361 + ], + "spans": [ + { + "bbox": [ + 104, + 347, + 507, + 361 + ], + "score": 1.0, + "content": "agent assistants; see for instance [57, 68]. To our knowledge, our FCP agents represent the state-", + "type": "text" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 358, + 505, + 371 + ], + "spans": [ + { + "bbox": [ + 105, + 358, + 505, + 371 + ], + "score": 1.0, + "content": "of-the-art in coordinating with novel human partners on an equal footing of capabilities in a rich", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 105, + 370, + 426, + 381 + ], + "spans": [ + { + "bbox": [ + 105, + 370, + 426, + 381 + ], + "score": 1.0, + "content": "gridworld environment, as measured by the challenge tasks in Carroll et al. [12].", + "type": "text" + } + ], + "index": 23 + } + ], + "index": 21.5, + "bbox_fs": [ + 104, + 335, + 507, + 381 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 386, + 505, + 464 + ], + "lines": [ + { + "bbox": [ + 105, + 384, + 505, + 399 + ], + "spans": [ + { + "bbox": [ + 105, + 384, + 505, + 399 + ], + "score": 1.0, + "content": "Diversity in multi-agent reinforcement learning In multi-agent reinforcement learning, agents", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 397, + 505, + 410 + ], + "spans": [ + { + "bbox": [ + 105, + 397, + 505, + 410 + ], + "score": 1.0, + "content": "that train with behaviorally diverse populations of game partners tend to demonstrate stronger", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 106, + 407, + 506, + 421 + ], + "spans": [ + { + "bbox": [ + 106, + 407, + 506, + 421 + ], + "score": 1.0, + "content": "performance than their self-play counterparts. For example, across a range of multi-agent games,", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 419, + 505, + 432 + ], + "spans": [ + { + "bbox": [ + 105, + 419, + 505, + 432 + ], + "score": 1.0, + "content": "generalization to held-out populations can be improved by training larger and more diverse populations", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 106, + 429, + 505, + 443 + ], + "spans": [ + { + "bbox": [ + 106, + 429, + 505, + 443 + ], + "score": 1.0, + "content": "[13, 42, 50]. In mixed-motive settings, cooperation among agents can be encouraged through social", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 441, + 505, + 453 + ], + "spans": [ + { + "bbox": [ + 106, + 441, + 505, + 453 + ], + "score": 1.0, + "content": "diversity, such as in player preferences and rewards [3, 47, 49]. Similarly, competitiveness can be", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 451, + 472, + 464 + ], + "spans": [ + { + "bbox": [ + 105, + 451, + 422, + 464 + ], + "score": 1.0, + "content": "optimized through selective matchmaking between increasingly diverse agents", + "type": "text" + }, + { + "bbox": [ + 423, + 451, + 469, + 463 + ], + "score": 0.6, + "content": "\\pm \\boxed { 1 2 4 } \\boxed { 3 9 } \\boxed { 6 9 }", + "type": "inline_equation" + }, + { + "bbox": [ + 470, + 451, + 472, + 464 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 30 + } + ], + "index": 27, + "bbox_fs": [ + 105, + 384, + 506, + 464 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 468, + 505, + 512 + ], + "lines": [ + { + "bbox": [ + 105, + 468, + 505, + 480 + ], + "spans": [ + { + "bbox": [ + 105, + 468, + 505, + 480 + ], + "score": 1.0, + "content": "Despite the increased focus on improving multi-agent performance, evaluation has typically been", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 479, + 505, + 492 + ], + "spans": [ + { + "bbox": [ + 105, + 479, + 505, + 492 + ], + "score": 1.0, + "content": "constrained to agent-agent settings. High-performing agents have infrequently been evaluated with", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 489, + 505, + 504 + ], + "spans": [ + { + "bbox": [ + 105, + 489, + 308, + 504 + ], + "score": 1.0, + "content": "humans, particularly in non-competitive domains", + "type": "text" + }, + { + "bbox": [ + 308, + 489, + 326, + 501 + ], + "score": 0.66, + "content": "\\dot { \\left. \\overline { { \\dot { \\left. \\dot { \\theta } \\right\\| } } } \\right. }", + "type": "inline_equation" + }, + { + "bbox": [ + 326, + 489, + 505, + 504 + ], + "score": 1.0, + "content": ". We add to this growing literature, showing", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 501, + 471, + 513 + ], + "spans": [ + { + "bbox": [ + 105, + 501, + 471, + 513 + ], + "score": 1.0, + "content": "that training with diversity is a powerful approach for effective human-agent collaboration.", + "type": "text" + } + ], + "index": 34 + } + ], + "index": 32.5, + "bbox_fs": [ + 105, + 468, + 505, + 513 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 517, + 505, + 595 + ], + "lines": [ + { + "bbox": [ + 105, + 515, + 506, + 531 + ], + "spans": [ + { + "bbox": [ + 105, + 515, + 506, + 531 + ], + "score": 1.0, + "content": "Human-agent interaction In recent years, increased attention has been directed toward designing", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 527, + 506, + 541 + ], + "spans": [ + { + "bbox": [ + 105, + 527, + 465, + 541 + ], + "score": 1.0, + "content": "machine learning agents capable of collaborating with humans [41, 57, 68, 72] (see also", + "type": "text" + }, + { + "bbox": [ + 465, + 528, + 482, + 540 + ], + "score": 0.76, + "content": "\\textcircled { 1 1 6 } \\textcircled { }", + "type": "inline_equation" + }, + { + "bbox": [ + 483, + 527, + 506, + 541 + ], + "score": 1.0, + "content": "for a", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 537, + 505, + 552 + ], + "spans": [ + { + "bbox": [ + 105, + 537, + 304, + 552 + ], + "score": 1.0, + "content": "broader review on Cooperative AI). Tylkin et al.", + "type": "text" + }, + { + "bbox": [ + 305, + 539, + 322, + 550 + ], + "score": 0.64, + "content": "\\lVert \\overline { { 6 8 } } \\rVert", + "type": "inline_equation" + }, + { + "bbox": [ + 323, + 537, + 505, + 552 + ], + "score": 1.0, + "content": "is particularly notable in also demonstrating", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 550, + 505, + 562 + ], + "spans": [ + { + "bbox": [ + 106, + 550, + 505, + 562 + ], + "score": 1.0, + "content": "that partially trained agents can be useful learning targets for human helpers, although in a different", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 106, + 561, + 505, + 573 + ], + "spans": [ + { + "bbox": [ + 106, + 561, + 505, + 573 + ], + "score": 1.0, + "content": "domain (cooperative Atari). Our method, FCP, can be seen as extending theirs by training with", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 572, + 506, + 583 + ], + "spans": [ + { + "bbox": [ + 105, + 572, + 506, + 583 + ], + "score": 1.0, + "content": "multiple “skill levels” and random seeds, rather than just one, which we demonstrate to be crucial to", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 583, + 331, + 595 + ], + "spans": [ + { + "bbox": [ + 105, + 583, + 331, + 595 + ], + "score": 1.0, + "content": "our agents’ performance (Tables 1 and 2 and Figure 7b).", + "type": "text" + } + ], + "index": 41 + } + ], + "index": 38, + "bbox_fs": [ + 105, + 515, + 506, + 595 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 599, + 505, + 666 + ], + "lines": [ + { + "bbox": [ + 106, + 599, + 506, + 612 + ], + "spans": [ + { + "bbox": [ + 106, + 599, + 343, + 612 + ], + "score": 1.0, + "content": "A key preceding entry in this research area is Carroll et al.", + "type": "text" + }, + { + "bbox": [ + 343, + 599, + 361, + 610 + ], + "score": 0.58, + "content": "[ \\mathbb { 1 2 } ]", + "type": "inline_equation" + }, + { + "bbox": [ + 361, + 599, + 506, + 612 + ], + "score": 1.0, + "content": ", who similarly investigated human-", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 609, + 507, + 623 + ], + "spans": [ + { + "bbox": [ + 105, + 609, + 507, + 623 + ], + "score": 1.0, + "content": "agent coordination in Overcooked. We use their method (BCP) as a baseline throughout our exper-", + "type": "text" + } + ], + "index": 43 + }, + { + "bbox": [ + 105, + 620, + 506, + 633 + ], + "spans": [ + { + "bbox": [ + 105, + 621, + 172, + 633 + ], + "score": 1.0, + "content": "iments (Section", + "type": "text" + }, + { + "bbox": [ + 172, + 620, + 191, + 633 + ], + "score": 0.44, + "content": "\\boxed { 2 . 2 }", + "type": "inline_equation" + }, + { + "bbox": [ + 191, + 621, + 506, + 633 + ], + "score": 1.0, + "content": ". Relative to BCP, our approach removes the need for the expensive step of", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 106, + 632, + 506, + 644 + ], + "spans": [ + { + "bbox": [ + 106, + 632, + 506, + 644 + ], + "score": 1.0, + "content": "human data collection for agent training. Furthermore, through our novel human-agent experimental", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 106, + 642, + 506, + 655 + ], + "spans": [ + { + "bbox": [ + 106, + 642, + 506, + 655 + ], + "score": 1.0, + "content": "design, we go beyond objective performance metrics to compare the subjective preferences that", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 105, + 654, + 441, + 666 + ], + "spans": [ + { + "bbox": [ + 105, + 654, + 441, + 666 + ], + "score": 1.0, + "content": "agents generate. For a detailed comparison of methods and results, see Appendix E.", + "type": "text" + } + ], + "index": 47 + } + ], + "index": 44.5, + "bbox_fs": [ + 105, + 599, + 507, + 666 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 684, + 305, + 697 + ], + "lines": [ + { + "bbox": [ + 104, + 682, + 306, + 700 + ], + "spans": [ + { + "bbox": [ + 104, + 682, + 306, + 700 + ], + "score": 1.0, + "content": "4 Zero-shot coordination with agents", + "type": "text" + } + ], + "index": 48 + } + ], + "index": 48 + }, + { + "type": "text", + "bbox": [ + 104, + 710, + 487, + 722 + ], + "lines": [ + { + "bbox": [ + 105, + 709, + 489, + 723 + ], + "spans": [ + { + "bbox": [ + 105, + 709, + 489, + 723 + ], + "score": 1.0, + "content": "In this section, we evaluate our FCP agent, its ablations, and the baselines with held-out agents.", + "type": "text" + } + ], + "index": 49 + } + ], + "index": 49, + "bbox_fs": [ + 105, + 709, + 489, + 723 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "title", + "bbox": [ + 107, + 72, + 406, + 84 + ], + "lines": [ + { + "bbox": [ + 105, + 70, + 407, + 87 + ], + "spans": [ + { + "bbox": [ + 105, + 70, + 407, + 87 + ], + "score": 1.0, + "content": "4.1 Evaluation method: collaborative evaluation with agent partners", + "type": "text" + } + ], + "index": 0 + } + ], + "index": 0 + }, + { + "type": "text", + "bbox": [ + 107, + 92, + 505, + 148 + ], + "lines": [ + { + "bbox": [ + 106, + 93, + 505, + 105 + ], + "spans": [ + { + "bbox": [ + 106, + 93, + 505, + 105 + ], + "score": 1.0, + "content": "Our primary concern in this work is generalization to novel human partners (as investigated in", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 106, + 103, + 506, + 116 + ], + "spans": [ + { + "bbox": [ + 106, + 103, + 138, + 116 + ], + "score": 1.0, + "content": "Section", + "type": "text" + }, + { + "bbox": [ + 138, + 103, + 148, + 116 + ], + "score": 0.35, + "content": "\\textcircled{5}", + "type": "inline_equation" + }, + { + "bbox": [ + 148, + 103, + 506, + 116 + ], + "score": 1.0, + "content": ". However, just as collecting human-human data for behavioral cloning is expensive, so", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 106, + 114, + 506, + 128 + ], + "spans": [ + { + "bbox": [ + 106, + 114, + 506, + 128 + ], + "score": 1.0, + "content": "too is evaluating agents with humans. Consequently, we instead use generalization to held-out agent", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 125, + 505, + 138 + ], + "spans": [ + { + "bbox": [ + 105, + 125, + 505, + 138 + ], + "score": 1.0, + "content": "partners as a cheap proxy of performance with humans. This is then used to guide our model selection", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 136, + 505, + 149 + ], + "spans": [ + { + "bbox": [ + 105, + 136, + 505, + 149 + ], + "score": 1.0, + "content": "process, allowing us to be more targeted with the agents we select for our human-agent evaluations.", + "type": "text" + } + ], + "index": 5 + } + ], + "index": 3 + }, + { + "type": "text", + "bbox": [ + 108, + 153, + 286, + 164 + ], + "lines": [ + { + "bbox": [ + 106, + 151, + 286, + 166 + ], + "spans": [ + { + "bbox": [ + 106, + 151, + 286, + 166 + ], + "score": 1.0, + "content": "We evaluate with three held-out populations:", + "type": "text" + } + ], + "index": 6 + } + ], + "index": 6 + }, + { + "type": "text", + "bbox": [ + 129, + 172, + 506, + 246 + ], + "lines": [ + { + "bbox": [ + 129, + 171, + 506, + 185 + ], + "spans": [ + { + "bbox": [ + 129, + 171, + 283, + 185 + ], + "score": 1.0, + "content": "1. A BC model trained on human data,", + "type": "text" + }, + { + "bbox": [ + 284, + 173, + 312, + 185 + ], + "score": 0.91, + "content": "H _ { \\mathrm { p r o x y } }", + "type": "inline_equation" + }, + { + "bbox": [ + 312, + 171, + 506, + 185 + ], + "score": 1.0, + "content": ", intended as a proxy of generalization to humans,", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 141, + 182, + 261, + 196 + ], + "spans": [ + { + "bbox": [ + 141, + 182, + 240, + 196 + ], + "score": 1.0, + "content": "as done by Carroll et al.", + "type": "text" + }, + { + "bbox": [ + 240, + 183, + 258, + 195 + ], + "score": 0.29, + "content": "[ \\overbrace { | 1 2 | }", + "type": "inline_equation" + }, + { + "bbox": [ + 258, + 182, + 261, + 196 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 128, + 196, + 507, + 211 + ], + "spans": [ + { + "bbox": [ + 128, + 196, + 507, + 211 + ], + "score": 1.0, + "content": "2. A set of self-play agents varying in seed, architecture, and training time (specifically, held-", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 141, + 207, + 505, + 222 + ], + "spans": [ + { + "bbox": [ + 141, + 208, + 207, + 222 + ], + "score": 1.0, + "content": "out seeds of the", + "type": "text" + }, + { + "bbox": [ + 207, + 209, + 241, + 219 + ], + "score": 0.89, + "content": "N = 3 2", + "type": "inline_equation" + }, + { + "bbox": [ + 241, + 208, + 335, + 222 + ], + "score": 1.0, + "content": "partners trained for the", + "type": "text" + }, + { + "bbox": [ + 335, + 209, + 367, + 220 + ], + "score": 0.9, + "content": "\\mathrm { F C P } _ { + A }", + "type": "inline_equation" + }, + { + "bbox": [ + 367, + 208, + 460, + 222 + ], + "score": 1.0, + "content": "agent; see Section 2.4).", + "type": "text" + }, + { + "bbox": [ + 462, + 207, + 505, + 221 + ], + "score": 1.0, + "content": "These are", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 142, + 219, + 423, + 232 + ], + "spans": [ + { + "bbox": [ + 142, + 219, + 423, + 232 + ], + "score": 1.0, + "content": "intended to test generalization to a diverse yet still skillful population.", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 129, + 234, + 464, + 246 + ], + "spans": [ + { + "bbox": [ + 129, + 234, + 464, + 246 + ], + "score": 1.0, + "content": "3. Randomly initialized agents intended to test generalization to low-skill partners.", + "type": "text" + } + ], + "index": 12 + } + ], + "index": 9.5 + }, + { + "type": "text", + "bbox": [ + 107, + 254, + 506, + 298 + ], + "lines": [ + { + "bbox": [ + 105, + 253, + 506, + 266 + ], + "spans": [ + { + "bbox": [ + 105, + 253, + 506, + 266 + ], + "score": 1.0, + "content": "For all results, we report the average number of deliveries made by both players within an episode,", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 264, + 506, + 277 + ], + "spans": [ + { + "bbox": [ + 105, + 264, + 330, + 277 + ], + "score": 1.0, + "content": "aggregated across the 5 different layouts from Figure", + "type": "text" + }, + { + "bbox": [ + 331, + 264, + 341, + 277 + ], + "score": 0.74, + "content": "\\boxed { 4 }", + "type": "inline_equation" + }, + { + "bbox": [ + 342, + 264, + 506, + 277 + ], + "score": 1.0, + "content": "(with the per-layout results reported in", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 275, + 505, + 288 + ], + "spans": [ + { + "bbox": [ + 105, + 276, + 146, + 288 + ], + "score": 1.0, + "content": "Appendix", + "type": "text" + }, + { + "bbox": [ + 146, + 275, + 166, + 288 + ], + "score": 0.51, + "content": "\\underline { { \\overline { { ( \\mathrm { C . 2 } ) } } } }", + "type": "inline_equation" + }, + { + "bbox": [ + 166, + 276, + 167, + 288 + ], + "score": 1.0, + "content": ".", + "type": "text" + }, + { + "bbox": [ + 168, + 275, + 505, + 288 + ], + "score": 1.0, + "content": "We estimate mean and standard deviation across 5 random seeds. For each seed, we", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 286, + 507, + 299 + ], + "spans": [ + { + "bbox": [ + 105, + 286, + 507, + 299 + ], + "score": 1.0, + "content": "evaluate the agent with all members of the held-out population for 10 episodes per agent-partner pair.", + "type": "text" + } + ], + "index": 16 + } + ], + "index": 14.5 + }, + { + "type": "title", + "bbox": [ + 107, + 310, + 162, + 322 + ], + "lines": [ + { + "bbox": [ + 105, + 309, + 163, + 323 + ], + "spans": [ + { + "bbox": [ + 105, + 309, + 163, + 323 + ], + "score": 1.0, + "content": "4.2 Results", + "type": "text" + } + ], + "index": 17 + } + ], + "index": 17 + }, + { + "type": "title", + "bbox": [ + 108, + 330, + 336, + 342 + ], + "lines": [ + { + "bbox": [ + 106, + 330, + 338, + 344 + ], + "spans": [ + { + "bbox": [ + 106, + 330, + 338, + 344 + ], + "score": 1.0, + "content": "Finding 1: FCP significantly outperforms all baselines", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 18 + }, + { + "type": "text", + "bbox": [ + 107, + 343, + 505, + 398 + ], + "lines": [ + { + "bbox": [ + 106, + 343, + 505, + 355 + ], + "spans": [ + { + "bbox": [ + 106, + 343, + 505, + 355 + ], + "score": 1.0, + "content": "To begin, we compare our FCP agent and the baselines when partnered with the three held-out", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 353, + 505, + 367 + ], + "spans": [ + { + "bbox": [ + 106, + 354, + 323, + 367 + ], + "score": 1.0, + "content": "populations introduced above. As can be seen in Figure", + "type": "text" + }, + { + "bbox": [ + 324, + 353, + 334, + 367 + ], + "score": 0.61, + "content": "\\boxed { 5 }", + "type": "inline_equation" + }, + { + "bbox": [ + 334, + 354, + 505, + 367 + ], + "score": 1.0, + "content": "FCP significantly outperforms all baselines", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 103, + 361, + 502, + 381 + ], + "spans": [ + { + "bbox": [ + 103, + 361, + 473, + 381 + ], + "score": 1.0, + "content": "when partnered with all three held-out populations. Notably, it performs better than BCP with", + "type": "text" + }, + { + "bbox": [ + 474, + 365, + 502, + 377 + ], + "score": 0.88, + "content": "H _ { \\mathrm { p r o x y } }", + "type": "inline_equation" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 375, + 506, + 389 + ], + "spans": [ + { + "bbox": [ + 105, + 375, + 451, + 389 + ], + "score": 1.0, + "content": "even though BCP trains with such a model and FCP does not. Similar to Carroll et al.", + "type": "text" + }, + { + "bbox": [ + 451, + 375, + 469, + 387 + ], + "score": 0.28, + "content": "\\mathbb { \\lVert 1 2 \\rVert }", + "type": "inline_equation" + }, + { + "bbox": [ + 469, + 375, + 506, + 389 + ], + "score": 1.0, + "content": ", we find", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 387, + 252, + 399 + ], + "spans": [ + { + "bbox": [ + 106, + 387, + 252, + 399 + ], + "score": 1.0, + "content": "that BCP significantly outscores SP.", + "type": "text" + } + ], + "index": 23 + } + ], + "index": 21 + }, + { + "type": "text", + "bbox": [ + 106, + 403, + 505, + 459 + ], + "lines": [ + { + "bbox": [ + 106, + 403, + 506, + 415 + ], + "spans": [ + { + "bbox": [ + 106, + 403, + 506, + 415 + ], + "score": 1.0, + "content": "When paired with a randomly initialized partner which behaves suboptimally, we see an even greater", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 413, + 506, + 426 + ], + "spans": [ + { + "bbox": [ + 105, + 413, + 506, + 426 + ], + "score": 1.0, + "content": "difference between FCP and the baselines. Given that FCP is trained with non-held-out versions of", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 424, + 506, + 439 + ], + "spans": [ + { + "bbox": [ + 105, + 424, + 506, + 439 + ], + "score": 1.0, + "content": "such agents, it may not be surprising that it does so well with partners that behave poorly. However,", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 436, + 506, + 448 + ], + "spans": [ + { + "bbox": [ + 106, + 436, + 506, + 448 + ], + "score": 1.0, + "content": "what is surprising is how brittle the other training methods are. This suggests that they may not", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 446, + 480, + 460 + ], + "spans": [ + { + "bbox": [ + 105, + 446, + 480, + 460 + ], + "score": 1.0, + "content": "perform well with humans who are not highly skilled players, which we will see in Section 5.", + "type": "text" + } + ], + "index": 28 + } + ], + "index": 26 + }, + { + "type": "image", + "bbox": [ + 101, + 470, + 495, + 559 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 101, + 470, + 495, + 559 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 101, + 470, + 495, + 559 + ], + "spans": [ + { + "bbox": [ + 101, + 470, + 495, + 559 + ], + "score": 0.969, + "type": "image", + "image_path": "8083dcac415b6ef9624e1aa4f428a0a33c3e9d47ce8903df3798f3e80ef0427c.jpg" + } + ] + } + ], + "index": 30, + "virtual_lines": [ + { + "bbox": [ + 101, + 470, + 495, + 499.6666666666667 + ], + "spans": [], + "index": 29 + }, + { + "bbox": [ + 101, + 499.6666666666667, + 495, + 529.3333333333334 + ], + "spans": [], + "index": 30 + }, + { + "bbox": [ + 101, + 529.3333333333334, + 495, + 559.0 + ], + "spans": [], + "index": 31 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 565, + 506, + 622 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 565, + 505, + 577 + ], + "spans": [ + { + "bbox": [ + 106, + 565, + 505, + 577 + ], + "score": 1.0, + "content": "Figure 5: Agent-agent collaborative evaluation: Performance of each agent when partnered with", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 106, + 575, + 505, + 588 + ], + "spans": [ + { + "bbox": [ + 106, + 576, + 277, + 588 + ], + "score": 1.0, + "content": "each of the held-out populations (Section", + "type": "text" + }, + { + "bbox": [ + 278, + 575, + 296, + 588 + ], + "score": 0.62, + "content": "4 . 1 )", + "type": "inline_equation" + }, + { + "bbox": [ + 297, + 576, + 387, + 588 + ], + "score": 1.0, + "content": "in episodes of length", + "type": "text" + }, + { + "bbox": [ + 387, + 576, + 426, + 586 + ], + "score": 0.9, + "content": "T = 5 4 0", + "type": "inline_equation" + }, + { + "bbox": [ + 426, + 576, + 505, + 588 + ], + "score": 1.0, + "content": ". Importantly, FCP", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 106, + 587, + 505, + 599 + ], + "spans": [ + { + "bbox": [ + 106, + 587, + 505, + 599 + ], + "score": 1.0, + "content": "scores higher than all baselines with a variety of test partners. Error bars represent standard deviation", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 596, + 506, + 610 + ], + "spans": [ + { + "bbox": [ + 105, + 596, + 506, + 610 + ], + "score": 1.0, + "content": "over five random training seeds. Plots aggregate data across kitchen layouts; results calculated by", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 607, + 302, + 623 + ], + "spans": [ + { + "bbox": [ + 106, + 607, + 302, + 623 + ], + "score": 1.0, + "content": "individual layout can be found in Appendix C.2.", + "type": "text" + } + ], + "index": 36 + } + ], + "index": 34 + } + ], + "index": 32.0 + }, + { + "type": "text", + "bbox": [ + 106, + 637, + 506, + 722 + ], + "lines": [ + { + "bbox": [ + 105, + 636, + 491, + 652 + ], + "spans": [ + { + "bbox": [ + 105, + 636, + 491, + 652 + ], + "score": 1.0, + "content": "Finding 2: Training with past checkpoints is the most beneficial variation for performance", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 651, + 505, + 663 + ], + "spans": [ + { + "bbox": [ + 106, + 651, + 505, + 663 + ], + "score": 1.0, + "content": "Next, we investigate how the different training partner variations influence FCP’s performance. In", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 104, + 660, + 506, + 676 + ], + "spans": [ + { + "bbox": [ + 104, + 660, + 317, + 676 + ], + "score": 1.0, + "content": "particular, we separately ablate the past checkpoints", + "type": "text" + }, + { + "bbox": [ + 318, + 662, + 332, + 673 + ], + "score": 0.72, + "content": "( T )", + "type": "inline_equation" + }, + { + "bbox": [ + 333, + 660, + 401, + 676 + ], + "score": 1.0, + "content": "and architecture", + "type": "text" + }, + { + "bbox": [ + 401, + 662, + 416, + 673 + ], + "score": 0.68, + "content": "( A )", + "type": "inline_equation" + }, + { + "bbox": [ + 416, + 660, + 506, + 676 + ], + "score": 1.0, + "content": "variations, evaluating", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 671, + 498, + 687 + ], + "spans": [ + { + "bbox": [ + 105, + 671, + 498, + 687 + ], + "score": 1.0, + "content": "them with the same partners as in Figure 5. The results of this evaluation are presented in Table 1.", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 687, + 506, + 703 + ], + "spans": [ + { + "bbox": [ + 105, + 687, + 210, + 703 + ], + "score": 1.0, + "content": "Comparing the FCP and", + "type": "text" + }, + { + "bbox": [ + 210, + 689, + 242, + 700 + ], + "score": 0.83, + "content": "\\mathrm { F C P } _ { - T }", + "type": "inline_equation" + }, + { + "bbox": [ + 243, + 687, + 506, + 703 + ], + "score": 1.0, + "content": "columns, we see that removing past checkpoints from training", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 699, + 505, + 714 + ], + "spans": [ + { + "bbox": [ + 105, + 699, + 353, + 714 + ], + "score": 1.0, + "content": "significantly reduces performance. Comparing the FCP and", + "type": "text" + }, + { + "bbox": [ + 353, + 700, + 385, + 712 + ], + "score": 0.9, + "content": "\\operatorname { F C P } _ { + A }", + "type": "inline_equation" + }, + { + "bbox": [ + 385, + 699, + 505, + 714 + ], + "score": 1.0, + "content": "columns, we see that adding", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 711, + 505, + 724 + ], + "spans": [ + { + "bbox": [ + 105, + 711, + 505, + 724 + ], + "score": 1.0, + "content": "architectural variation to the training population offers no improvement over training with past", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 40 + } + ], + "page_idx": 5, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 302, + 742, + 309, + 750 + ], + "lines": [ + { + "bbox": [ + 302, + 741, + 310, + 752 + ], + "spans": [ + { + "bbox": [ + 302, + 741, + 310, + 752 + ], + "score": 1.0, + "content": "6", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "title", + "bbox": [ + 107, + 72, + 406, + 84 + ], + "lines": [ + { + "bbox": [ + 105, + 70, + 407, + 87 + ], + "spans": [ + { + "bbox": [ + 105, + 70, + 407, + 87 + ], + "score": 1.0, + "content": "4.1 Evaluation method: collaborative evaluation with agent partners", + "type": "text" + } + ], + "index": 0 + } + ], + "index": 0 + }, + { + "type": "text", + "bbox": [ + 107, + 92, + 505, + 148 + ], + "lines": [ + { + "bbox": [ + 106, + 93, + 505, + 105 + ], + "spans": [ + { + "bbox": [ + 106, + 93, + 505, + 105 + ], + "score": 1.0, + "content": "Our primary concern in this work is generalization to novel human partners (as investigated in", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 106, + 103, + 506, + 116 + ], + "spans": [ + { + "bbox": [ + 106, + 103, + 138, + 116 + ], + "score": 1.0, + "content": "Section", + "type": "text" + }, + { + "bbox": [ + 138, + 103, + 148, + 116 + ], + "score": 0.35, + "content": "\\textcircled{5}", + "type": "inline_equation" + }, + { + "bbox": [ + 148, + 103, + 506, + 116 + ], + "score": 1.0, + "content": ". However, just as collecting human-human data for behavioral cloning is expensive, so", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 106, + 114, + 506, + 128 + ], + "spans": [ + { + "bbox": [ + 106, + 114, + 506, + 128 + ], + "score": 1.0, + "content": "too is evaluating agents with humans. Consequently, we instead use generalization to held-out agent", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 125, + 505, + 138 + ], + "spans": [ + { + "bbox": [ + 105, + 125, + 505, + 138 + ], + "score": 1.0, + "content": "partners as a cheap proxy of performance with humans. This is then used to guide our model selection", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 136, + 505, + 149 + ], + "spans": [ + { + "bbox": [ + 105, + 136, + 505, + 149 + ], + "score": 1.0, + "content": "process, allowing us to be more targeted with the agents we select for our human-agent evaluations.", + "type": "text" + } + ], + "index": 5 + } + ], + "index": 3, + "bbox_fs": [ + 105, + 93, + 506, + 149 + ] + }, + { + "type": "text", + "bbox": [ + 108, + 153, + 286, + 164 + ], + "lines": [ + { + "bbox": [ + 106, + 151, + 286, + 166 + ], + "spans": [ + { + "bbox": [ + 106, + 151, + 286, + 166 + ], + "score": 1.0, + "content": "We evaluate with three held-out populations:", + "type": "text" + } + ], + "index": 6 + } + ], + "index": 6, + "bbox_fs": [ + 106, + 151, + 286, + 166 + ] + }, + { + "type": "text", + "bbox": [ + 129, + 172, + 506, + 246 + ], + "lines": [ + { + "bbox": [ + 129, + 171, + 506, + 185 + ], + "spans": [ + { + "bbox": [ + 129, + 171, + 283, + 185 + ], + "score": 1.0, + "content": "1. A BC model trained on human data,", + "type": "text" + }, + { + "bbox": [ + 284, + 173, + 312, + 185 + ], + "score": 0.91, + "content": "H _ { \\mathrm { p r o x y } }", + "type": "inline_equation" + }, + { + "bbox": [ + 312, + 171, + 506, + 185 + ], + "score": 1.0, + "content": ", intended as a proxy of generalization to humans,", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 141, + 182, + 261, + 196 + ], + "spans": [ + { + "bbox": [ + 141, + 182, + 240, + 196 + ], + "score": 1.0, + "content": "as done by Carroll et al.", + "type": "text" + }, + { + "bbox": [ + 240, + 183, + 258, + 195 + ], + "score": 0.29, + "content": "[ \\overbrace { | 1 2 | }", + "type": "inline_equation" + }, + { + "bbox": [ + 258, + 182, + 261, + 196 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 128, + 196, + 507, + 211 + ], + "spans": [ + { + "bbox": [ + 128, + 196, + 507, + 211 + ], + "score": 1.0, + "content": "2. A set of self-play agents varying in seed, architecture, and training time (specifically, held-", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 141, + 207, + 505, + 222 + ], + "spans": [ + { + "bbox": [ + 141, + 208, + 207, + 222 + ], + "score": 1.0, + "content": "out seeds of the", + "type": "text" + }, + { + "bbox": [ + 207, + 209, + 241, + 219 + ], + "score": 0.89, + "content": "N = 3 2", + "type": "inline_equation" + }, + { + "bbox": [ + 241, + 208, + 335, + 222 + ], + "score": 1.0, + "content": "partners trained for the", + "type": "text" + }, + { + "bbox": [ + 335, + 209, + 367, + 220 + ], + "score": 0.9, + "content": "\\mathrm { F C P } _ { + A }", + "type": "inline_equation" + }, + { + "bbox": [ + 367, + 208, + 460, + 222 + ], + "score": 1.0, + "content": "agent; see Section 2.4).", + "type": "text" + }, + { + "bbox": [ + 462, + 207, + 505, + 221 + ], + "score": 1.0, + "content": "These are", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 142, + 219, + 423, + 232 + ], + "spans": [ + { + "bbox": [ + 142, + 219, + 423, + 232 + ], + "score": 1.0, + "content": "intended to test generalization to a diverse yet still skillful population.", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 129, + 234, + 464, + 246 + ], + "spans": [ + { + "bbox": [ + 129, + 234, + 464, + 246 + ], + "score": 1.0, + "content": "3. Randomly initialized agents intended to test generalization to low-skill partners.", + "type": "text" + } + ], + "index": 12 + } + ], + "index": 9.5, + "bbox_fs": [ + 128, + 171, + 507, + 246 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 254, + 506, + 298 + ], + "lines": [ + { + "bbox": [ + 105, + 253, + 506, + 266 + ], + "spans": [ + { + "bbox": [ + 105, + 253, + 506, + 266 + ], + "score": 1.0, + "content": "For all results, we report the average number of deliveries made by both players within an episode,", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 264, + 506, + 277 + ], + "spans": [ + { + "bbox": [ + 105, + 264, + 330, + 277 + ], + "score": 1.0, + "content": "aggregated across the 5 different layouts from Figure", + "type": "text" + }, + { + "bbox": [ + 331, + 264, + 341, + 277 + ], + "score": 0.74, + "content": "\\boxed { 4 }", + "type": "inline_equation" + }, + { + "bbox": [ + 342, + 264, + 506, + 277 + ], + "score": 1.0, + "content": "(with the per-layout results reported in", + "type": "text" + } + ], + "index": 14 + }, + { + "bbox": [ + 105, + 275, + 505, + 288 + ], + "spans": [ + { + "bbox": [ + 105, + 276, + 146, + 288 + ], + "score": 1.0, + "content": "Appendix", + "type": "text" + }, + { + "bbox": [ + 146, + 275, + 166, + 288 + ], + "score": 0.51, + "content": "\\underline { { \\overline { { ( \\mathrm { C . 2 } ) } } } }", + "type": "inline_equation" + }, + { + "bbox": [ + 166, + 276, + 167, + 288 + ], + "score": 1.0, + "content": ".", + "type": "text" + }, + { + "bbox": [ + 168, + 275, + 505, + 288 + ], + "score": 1.0, + "content": "We estimate mean and standard deviation across 5 random seeds. For each seed, we", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 286, + 507, + 299 + ], + "spans": [ + { + "bbox": [ + 105, + 286, + 507, + 299 + ], + "score": 1.0, + "content": "evaluate the agent with all members of the held-out population for 10 episodes per agent-partner pair.", + "type": "text" + } + ], + "index": 16 + } + ], + "index": 14.5, + "bbox_fs": [ + 105, + 253, + 507, + 299 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 310, + 162, + 322 + ], + "lines": [ + { + "bbox": [ + 105, + 309, + 163, + 323 + ], + "spans": [ + { + "bbox": [ + 105, + 309, + 163, + 323 + ], + "score": 1.0, + "content": "4.2 Results", + "type": "text" + } + ], + "index": 17 + } + ], + "index": 17 + }, + { + "type": "title", + "bbox": [ + 108, + 330, + 336, + 342 + ], + "lines": [ + { + "bbox": [ + 106, + 330, + 338, + 344 + ], + "spans": [ + { + "bbox": [ + 106, + 330, + 338, + 344 + ], + "score": 1.0, + "content": "Finding 1: FCP significantly outperforms all baselines", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 18 + }, + { + "type": "text", + "bbox": [ + 107, + 343, + 505, + 398 + ], + "lines": [ + { + "bbox": [ + 106, + 343, + 505, + 355 + ], + "spans": [ + { + "bbox": [ + 106, + 343, + 505, + 355 + ], + "score": 1.0, + "content": "To begin, we compare our FCP agent and the baselines when partnered with the three held-out", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 106, + 353, + 505, + 367 + ], + "spans": [ + { + "bbox": [ + 106, + 354, + 323, + 367 + ], + "score": 1.0, + "content": "populations introduced above. As can be seen in Figure", + "type": "text" + }, + { + "bbox": [ + 324, + 353, + 334, + 367 + ], + "score": 0.61, + "content": "\\boxed { 5 }", + "type": "inline_equation" + }, + { + "bbox": [ + 334, + 354, + 505, + 367 + ], + "score": 1.0, + "content": "FCP significantly outperforms all baselines", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 103, + 361, + 502, + 381 + ], + "spans": [ + { + "bbox": [ + 103, + 361, + 473, + 381 + ], + "score": 1.0, + "content": "when partnered with all three held-out populations. Notably, it performs better than BCP with", + "type": "text" + }, + { + "bbox": [ + 474, + 365, + 502, + 377 + ], + "score": 0.88, + "content": "H _ { \\mathrm { p r o x y } }", + "type": "inline_equation" + } + ], + "index": 21 + }, + { + "bbox": [ + 105, + 375, + 506, + 389 + ], + "spans": [ + { + "bbox": [ + 105, + 375, + 451, + 389 + ], + "score": 1.0, + "content": "even though BCP trains with such a model and FCP does not. Similar to Carroll et al.", + "type": "text" + }, + { + "bbox": [ + 451, + 375, + 469, + 387 + ], + "score": 0.28, + "content": "\\mathbb { \\lVert 1 2 \\rVert }", + "type": "inline_equation" + }, + { + "bbox": [ + 469, + 375, + 506, + 389 + ], + "score": 1.0, + "content": ", we find", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 387, + 252, + 399 + ], + "spans": [ + { + "bbox": [ + 106, + 387, + 252, + 399 + ], + "score": 1.0, + "content": "that BCP significantly outscores SP.", + "type": "text" + } + ], + "index": 23 + } + ], + "index": 21, + "bbox_fs": [ + 103, + 343, + 506, + 399 + ] + }, + { + "type": "text", + "bbox": [ + 106, + 403, + 505, + 459 + ], + "lines": [ + { + "bbox": [ + 106, + 403, + 506, + 415 + ], + "spans": [ + { + "bbox": [ + 106, + 403, + 506, + 415 + ], + "score": 1.0, + "content": "When paired with a randomly initialized partner which behaves suboptimally, we see an even greater", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 105, + 413, + 506, + 426 + ], + "spans": [ + { + "bbox": [ + 105, + 413, + 506, + 426 + ], + "score": 1.0, + "content": "difference between FCP and the baselines. Given that FCP is trained with non-held-out versions of", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 424, + 506, + 439 + ], + "spans": [ + { + "bbox": [ + 105, + 424, + 506, + 439 + ], + "score": 1.0, + "content": "such agents, it may not be surprising that it does so well with partners that behave poorly. However,", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 436, + 506, + 448 + ], + "spans": [ + { + "bbox": [ + 106, + 436, + 506, + 448 + ], + "score": 1.0, + "content": "what is surprising is how brittle the other training methods are. This suggests that they may not", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 446, + 480, + 460 + ], + "spans": [ + { + "bbox": [ + 105, + 446, + 480, + 460 + ], + "score": 1.0, + "content": "perform well with humans who are not highly skilled players, which we will see in Section 5.", + "type": "text" + } + ], + "index": 28 + } + ], + "index": 26, + "bbox_fs": [ + 105, + 403, + 506, + 460 + ] + }, + { + "type": "image", + "bbox": [ + 101, + 470, + 495, + 559 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 101, + 470, + 495, + 559 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 101, + 470, + 495, + 559 + ], + "spans": [ + { + "bbox": [ + 101, + 470, + 495, + 559 + ], + "score": 0.969, + "type": "image", + "image_path": "8083dcac415b6ef9624e1aa4f428a0a33c3e9d47ce8903df3798f3e80ef0427c.jpg" + } + ] + } + ], + "index": 30, + "virtual_lines": [ + { + "bbox": [ + 101, + 470, + 495, + 499.6666666666667 + ], + "spans": [], + "index": 29 + }, + { + "bbox": [ + 101, + 499.6666666666667, + 495, + 529.3333333333334 + ], + "spans": [], + "index": 30 + }, + { + "bbox": [ + 101, + 529.3333333333334, + 495, + 559.0 + ], + "spans": [], + "index": 31 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 565, + 506, + 622 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 565, + 505, + 577 + ], + "spans": [ + { + "bbox": [ + 106, + 565, + 505, + 577 + ], + "score": 1.0, + "content": "Figure 5: Agent-agent collaborative evaluation: Performance of each agent when partnered with", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 106, + 575, + 505, + 588 + ], + "spans": [ + { + "bbox": [ + 106, + 576, + 277, + 588 + ], + "score": 1.0, + "content": "each of the held-out populations (Section", + "type": "text" + }, + { + "bbox": [ + 278, + 575, + 296, + 588 + ], + "score": 0.62, + "content": "4 . 1 )", + "type": "inline_equation" + }, + { + "bbox": [ + 297, + 576, + 387, + 588 + ], + "score": 1.0, + "content": "in episodes of length", + "type": "text" + }, + { + "bbox": [ + 387, + 576, + 426, + 586 + ], + "score": 0.9, + "content": "T = 5 4 0", + "type": "inline_equation" + }, + { + "bbox": [ + 426, + 576, + 505, + 588 + ], + "score": 1.0, + "content": ". Importantly, FCP", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 106, + 587, + 505, + 599 + ], + "spans": [ + { + "bbox": [ + 106, + 587, + 505, + 599 + ], + "score": 1.0, + "content": "scores higher than all baselines with a variety of test partners. Error bars represent standard deviation", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 596, + 506, + 610 + ], + "spans": [ + { + "bbox": [ + 105, + 596, + 506, + 610 + ], + "score": 1.0, + "content": "over five random training seeds. Plots aggregate data across kitchen layouts; results calculated by", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 106, + 607, + 302, + 623 + ], + "spans": [ + { + "bbox": [ + 106, + 607, + 302, + 623 + ], + "score": 1.0, + "content": "individual layout can be found in Appendix C.2.", + "type": "text" + } + ], + "index": 36 + } + ], + "index": 34 + } + ], + "index": 32.0 + }, + { + "type": "text", + "bbox": [ + 106, + 637, + 506, + 722 + ], + "lines": [ + { + "bbox": [ + 105, + 636, + 491, + 652 + ], + "spans": [ + { + "bbox": [ + 105, + 636, + 491, + 652 + ], + "score": 1.0, + "content": "Finding 2: Training with past checkpoints is the most beneficial variation for performance", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 106, + 651, + 505, + 663 + ], + "spans": [ + { + "bbox": [ + 106, + 651, + 505, + 663 + ], + "score": 1.0, + "content": "Next, we investigate how the different training partner variations influence FCP’s performance. In", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 104, + 660, + 506, + 676 + ], + "spans": [ + { + "bbox": [ + 104, + 660, + 317, + 676 + ], + "score": 1.0, + "content": "particular, we separately ablate the past checkpoints", + "type": "text" + }, + { + "bbox": [ + 318, + 662, + 332, + 673 + ], + "score": 0.72, + "content": "( T )", + "type": "inline_equation" + }, + { + "bbox": [ + 333, + 660, + 401, + 676 + ], + "score": 1.0, + "content": "and architecture", + "type": "text" + }, + { + "bbox": [ + 401, + 662, + 416, + 673 + ], + "score": 0.68, + "content": "( A )", + "type": "inline_equation" + }, + { + "bbox": [ + 416, + 660, + 506, + 676 + ], + "score": 1.0, + "content": "variations, evaluating", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 671, + 498, + 687 + ], + "spans": [ + { + "bbox": [ + 105, + 671, + 498, + 687 + ], + "score": 1.0, + "content": "them with the same partners as in Figure 5. The results of this evaluation are presented in Table 1.", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 687, + 506, + 703 + ], + "spans": [ + { + "bbox": [ + 105, + 687, + 210, + 703 + ], + "score": 1.0, + "content": "Comparing the FCP and", + "type": "text" + }, + { + "bbox": [ + 210, + 689, + 242, + 700 + ], + "score": 0.83, + "content": "\\mathrm { F C P } _ { - T }", + "type": "inline_equation" + }, + { + "bbox": [ + 243, + 687, + 506, + 703 + ], + "score": 1.0, + "content": "columns, we see that removing past checkpoints from training", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 699, + 505, + 714 + ], + "spans": [ + { + "bbox": [ + 105, + 699, + 353, + 714 + ], + "score": 1.0, + "content": "significantly reduces performance. 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PartnerFCPFCP-TFCP+AFCP-T,+A
Hproxy10.6± 0.54.7± 0.49.9±0.67.0±0.8
Diverse SP11.2 ± 0.16.9 ± 0.111.1 ± 0.48.6 ± 0.4
Random8.6± 0.21.0 ± 0.18.4±0.43.2 ± 0.5
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Our statistical analysis below", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 631, + 505, + 644 + ], + "spans": [ + { + "bbox": [ + 105, + 631, + 505, + 644 + ], + "score": 1.0, + "content": "primarily relies upon the repeated-measures analysis of variance (ANOVA) method. 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PartnerFCPFCP-TFCP+AFCP-T,+A
Hproxy10.6± 0.54.7± 0.49.9±0.67.0±0.8
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In this", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 282, + 506, + 295 + ], + "spans": [ + { + "bbox": [ + 105, + 282, + 506, + 295 + ], + "score": 1.0, + "content": "section, we run an online study to evaluate our FCP agent and the baseline agents in collaborative", + "type": "text" + } + ], + "index": 13 + }, + { + "bbox": [ + 105, + 293, + 213, + 305 + ], + "spans": [ + { + "bbox": [ + 105, + 293, + 213, + 305 + ], + "score": 1.0, + "content": "play with human partners.", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 13, + "bbox_fs": [ + 105, + 271, + 506, + 305 + ] + }, + { + "type": "image", + "bbox": [ + 91, + 315, + 521, + 401 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 91, + 315, + 521, + 401 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 91, + 315, + 521, + 401 + ], + "spans": [ + { + "bbox": [ + 91, + 315, + 521, + 401 + ], + "score": 0.814, + "type": "image", + "image_path": "5856cbdd90eeef2a7a6093668472063862a759746c339aa0324e78efb5d67b22.jpg" + } + ] + } + ], + "index": 16, + "virtual_lines": [ + { + "bbox": [ + 91, + 315, + 521, + 343.6666666666667 + ], + "spans": [], + "index": 15 + }, + { + "bbox": [ + 91, + 343.6666666666667, + 521, + 372.33333333333337 + ], + "spans": [], + "index": 16 + }, + { + "bbox": [ + 91, + 372.33333333333337, + 521, + 401.00000000000006 + ], + "spans": [], + "index": 17 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 410, + 506, + 454 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 106, + 410, + 506, + 423 + ], + "spans": [ + { + "bbox": [ + 106, + 410, + 506, + 423 + ], + "score": 1.0, + "content": "Figure 6: Human-agent collaborative study: For our human-agent collaboration study, we recruited", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 420, + 505, + 433 + ], + "spans": [ + { + "bbox": [ + 105, + 420, + 505, + 433 + ], + "score": 1.0, + "content": "participants online to play games with FCP and baseline agents. Participants played a randomized", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 432, + 507, + 444 + ], + "spans": [ + { + "bbox": [ + 105, + 432, + 507, + 444 + ], + "score": 1.0, + "content": "sequence of episodes with different agent partners and kitchen layouts. After every two episodes,", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 105, + 442, + 498, + 455 + ], + "spans": [ + { + "bbox": [ + 105, + 442, + 498, + 455 + ], + "score": 1.0, + "content": "participants reported the direction and strength of their preference between their last two partners.", + "type": "text" + } + ], + "index": 21 + } + ], + "index": 19.5 + } + ], + "index": 17.75 + }, + { + "type": "title", + "bbox": [ + 106, + 474, + 427, + 486 + ], + "lines": [ + { + "bbox": [ + 105, + 473, + 429, + 489 + ], + "spans": [ + { + "bbox": [ + 105, + 473, + 429, + 489 + ], + "score": 1.0, + "content": "5.1 Evaluation method: collaborative evaluation with human participants", + "type": "text" + } + ], + "index": 22 + } + ], + "index": 22 + }, + { + "type": "text", + "bbox": [ + 107, + 495, + 506, + 550 + ], + "lines": [ + { + "bbox": [ + 105, + 493, + 506, + 508 + ], + "spans": [ + { + "bbox": [ + 105, + 493, + 506, + 508 + ], + "score": 1.0, + "content": "To test how effectively FCP’s performance generalizes to human partners, we recruited participants", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 106, + 505, + 505, + 518 + ], + "spans": [ + { + "bbox": [ + 106, + 505, + 159, + 518 + ], + "score": 1.0, + "content": "from Prolific", + "type": "text" + }, + { + "bbox": [ + 160, + 505, + 192, + 517 + ], + "score": 0.31, + "content": "\\mathbb { 1 1 8 } , \\lvert 5 5 \\rvert", + "type": "inline_equation" + }, + { + "bbox": [ + 193, + 505, + 328, + 518 + ], + "score": 1.0, + "content": "for an online collaboration study", + "type": "text" + }, + { + "bbox": [ + 329, + 506, + 367, + 516 + ], + "score": 0.87, + "content": "N = 1 1 4", + "type": "inline_equation" + }, + { + "bbox": [ + 367, + 505, + 371, + 518 + ], + "score": 1.0, + "content": ";", + "type": "text" + }, + { + "bbox": [ + 371, + 506, + 398, + 516 + ], + "score": 0.83, + "content": "3 7 . 7 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 398, + 505, + 430, + 518 + ], + "score": 1.0, + "content": "female,", + "type": "text" + }, + { + "bbox": [ + 431, + 506, + 457, + 516 + ], + "score": 0.86, + "content": "5 9 . 6 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 458, + 505, + 483, + 518 + ], + "score": 1.0, + "content": "male,", + "type": "text" + }, + { + "bbox": [ + 483, + 506, + 505, + 516 + ], + "score": 0.85, + "content": "1 . 8 \\%", + "type": "inline_equation" + } + ], + "index": 24 + }, + { + "bbox": [ + 106, + 516, + 506, + 529 + ], + "spans": [ + { + "bbox": [ + 106, + 516, + 506, + 529 + ], + "score": 1.0, + "content": "nonbinary; median age between 25–34 years). We used a within-participant design for the study:", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 527, + 506, + 541 + ], + "spans": [ + { + "bbox": [ + 105, + 527, + 506, + 541 + ], + "score": 1.0, + "content": "each participant played with a full cohort of agents (i.e. generated through every training method).", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 537, + 492, + 553 + ], + "spans": [ + { + "bbox": [ + 105, + 537, + 492, + 553 + ], + "score": 1.0, + "content": "This design allowed us to evaluate both objective performance as well as subjective preferences.", + "type": "text" + } + ], + "index": 27 + } + ], + "index": 25, + "bbox_fs": [ + 105, + 493, + 506, + 553 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 554, + 505, + 654 + ], + "lines": [ + { + "bbox": [ + 106, + 554, + 505, + 567 + ], + "spans": [ + { + "bbox": [ + 106, + 554, + 505, + 567 + ], + "score": 1.0, + "content": "Participants first read game instructions and played a short tutorial episode guiding them through", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 565, + 506, + 579 + ], + "spans": [ + { + "bbox": [ + 106, + 565, + 297, + 578 + ], + "score": 1.0, + "content": "the dish preparation sequence (see Appendix", + "type": "text" + }, + { + "bbox": [ + 297, + 565, + 325, + 579 + ], + "score": 0.78, + "content": "\\underline { { \\overline { { \\mathbb { D . 1 . 1 } } } } }", + "type": "inline_equation" + }, + { + "bbox": [ + 325, + 565, + 506, + 578 + ], + "score": 1.0, + "content": "for instruction text and study screenshots).", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 576, + 505, + 590 + ], + "spans": [ + { + "bbox": [ + 105, + 576, + 505, + 590 + ], + "score": 1.0, + "content": "Participants then played 20 episodes with a randomized sequence of agent partners and kitchen", + "type": "text" + } + ], + "index": 30 + }, + { + "bbox": [ + 105, + 587, + 505, + 600 + ], + "spans": [ + { + "bbox": [ + 105, + 587, + 209, + 600 + ], + "score": 1.0, + "content": "layouts. Episodes lasted", + "type": "text" + }, + { + "bbox": [ + 210, + 588, + 249, + 598 + ], + "score": 0.89, + "content": "T = 3 0 0", + "type": "inline_equation" + }, + { + "bbox": [ + 249, + 587, + 505, + 600 + ], + "score": 1.0, + "content": "steps (1 minute) each. 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Our statistical analysis below", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 631, + 505, + 644 + ], + "spans": [ + { + "bbox": [ + 105, + 631, + 505, + 644 + ], + "score": 1.0, + "content": "primarily relies upon the repeated-measures analysis of variance (ANOVA) method. See Appendix D", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 641, + 476, + 655 + ], + "spans": [ + { + "bbox": [ + 105, + 641, + 476, + 655 + ], + "score": 1.0, + "content": "for additional details of our study design and analysis, including independent ethical review.", + "type": "text" + } + ], + "index": 36 + } + ], + "index": 32, + "bbox_fs": [ + 105, + 554, + 506, + 655 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 667, + 162, + 678 + ], + "lines": [ + { + "bbox": [ + 105, + 666, + 163, + 680 + ], + "spans": [ + { + "bbox": [ + 105, + 666, + 163, + 680 + ], + "score": 1.0, + "content": "5.2 Results", + "type": "text" + } + ], + "index": 37 + } + ], + "index": 37 + }, + { + "type": "text", + "bbox": [ + 105, + 687, + 506, + 722 + ], + "lines": [ + { + "bbox": [ + 105, + 686, + 477, + 700 + ], + "spans": [ + { + "bbox": [ + 105, + 686, + 477, + 700 + ], + "score": 1.0, + "content": "Finding 1: FCP coordinates best with humans, achieving the highest score across maps", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 699, + 507, + 714 + ], + "spans": [ + { + "bbox": [ + 105, + 699, + 507, + 714 + ], + "score": 1.0, + "content": "To begin, we compare the objective team performance supported by our FCP and baseline agents.", + "type": "text" + } + ], + "index": 39 + }, + { + "bbox": [ + 105, + 710, + 507, + 724 + ], + "spans": [ + { + "bbox": [ + 105, + 710, + 507, + 724 + ], + "score": 1.0, + "content": "The strong FCP performance observed in agent-agent play generalizes to human-agent collaboration:", + "type": "text" + } + ], + "index": 40 + } + ], + "index": 39, + "bbox_fs": [ + 105, + 686, + 507, + 724 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "text", + "bbox": [ + 106, + 72, + 505, + 139 + ], + "lines": [ + { + "bbox": [ + 106, + 73, + 505, + 84 + ], + "spans": [ + { + "bbox": [ + 106, + 73, + 505, + 84 + ], + "score": 1.0, + "content": "the FCP-human teams significantly outperform all other agent-human teams, achieving the highest", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 106, + 83, + 506, + 97 + ], + "spans": [ + { + "bbox": [ + 106, + 83, + 244, + 97 + ], + "score": 1.0, + "content": "average scores across maps, every", + "type": "text" + }, + { + "bbox": [ + 244, + 84, + 286, + 95 + ], + "score": 0.9, + "content": "p < 0 . 0 0 1", + "type": "inline_equation" + }, + { + "bbox": [ + 287, + 83, + 318, + 97 + ], + "score": 1.0, + "content": "(Figure", + "type": "text" + }, + { + "bbox": [ + 318, + 83, + 333, + 95 + ], + "score": 0.81, + "content": "\\lvert \\overline { { 7 \\mathrm { a } } } \\rvert", + "type": "inline_equation" + }, + { + "bbox": [ + 333, + 83, + 506, + 97 + ], + "score": 1.0, + "content": ", while performing as well as or better than", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 106, + 94, + 505, + 107 + ], + "spans": [ + { + "bbox": [ + 106, + 94, + 319, + 107 + ], + "score": 1.0, + "content": "the other teams on each individual map (see Appendix", + "type": "text" + }, + { + "bbox": [ + 319, + 95, + 339, + 107 + ], + "score": 0.6, + "content": "\\mathbf { D } . 3 )", + "type": "inline_equation" + }, + { + "bbox": [ + 339, + 94, + 505, + 107 + ], + "score": 1.0, + "content": ". 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Plots aggregate data across kitchen layouts; results calculated by individual layout can", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 389, + 216, + 405 + ], + "spans": [ + { + "bbox": [ + 105, + 389, + 216, + 405 + ], + "score": 1.0, + "content": "be found in Appendix D.3.", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 16.5 + } + ], + "index": 14.75 + }, + { + "type": "title", + "bbox": [ + 107, + 421, + 266, + 433 + ], + "lines": [ + { + "bbox": [ + 106, + 420, + 267, + 435 + ], + "spans": [ + { + "bbox": [ + 106, + 420, + 267, + 435 + ], + "score": 1.0, + "content": "5.3 Exploratory behavioral analysis", + "type": "text" + } + ], + "index": 19 + } + ], + "index": 19 + }, + { + "type": "text", + "bbox": [ + 106, + 441, + 505, + 464 + ], + "lines": [ + { + "bbox": [ + 105, + 440, + 505, + 454 + ], + "spans": [ + { + "bbox": [ + 105, + 440, + 505, + 454 + ], + "score": 1.0, + "content": "To better understand how the human-agent scores and preferences may have arisen, here we analyze", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 106, + 452, + 437, + 465 + ], + "spans": [ + { + "bbox": [ + 106, + 452, + 437, + 465 + ], + "score": 1.0, + "content": "the resulting action trajectories of each human and agent player in our experiment.", + "type": "text" + } + ], + "index": 21 + } + ], + "index": 20.5 + }, + { + "type": "image", + "bbox": [ + 138, + 478, + 474, + 597 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 138, + 478, + 474, + 597 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 138, + 478, + 474, + 597 + ], + "spans": [ + { + "bbox": [ + 138, + 478, + 474, + 597 + ], + "score": 0.519, + "type": "image", + "image_path": "65959c0ab23a18c0f8de72bc451d199e7d9d9cc2698e7ca2ac4f320e26dac829.jpg" + } + ] + } + ], + "index": 23, + "virtual_lines": [ + { + "bbox": [ + 138, + 478, + 474, + 517.6666666666666 + ], + "spans": [], + "index": 22 + }, + { + "bbox": [ + 138, + 517.6666666666666, + 474, + 557.3333333333333 + ], + "spans": [], + "index": 23 + }, + { + "bbox": [ + 138, + 557.3333333333333, + 474, + 596.9999999999999 + ], + "spans": [], + "index": 24 + } + ] + } + ], + "index": 23 + }, + { + "type": "text", + "bbox": [ + 106, + 601, + 506, + 657 + ], + "lines": [ + { + "bbox": [ + 105, + 601, + 507, + 614 + ], + "spans": [ + { + "bbox": [ + 105, + 601, + 412, + 614 + ], + "score": 1.0, + "content": "Figure 8: Behavioral analysis: (a) FCP is able to move most frequently", + "type": "text" + }, + { + "bbox": [ + 412, + 601, + 432, + 613 + ], + "score": 0.85, + "content": "3 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 432, + 601, + 507, + 614 + ], + "score": 1.0, + "content": "of the time), cor-", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 613, + 506, + 626 + ], + "spans": [ + { + "bbox": [ + 105, + 613, + 506, + 626 + ], + "score": 1.0, + "content": "responding to the best movement coordination with human partners. (b) FCP exhibits the most", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 624, + 506, + 636 + ], + "spans": [ + { + "bbox": [ + 105, + 624, + 506, + 636 + ], + "score": 1.0, + "content": "equal preferences over cooking pots (0.11 difference), aligning with human preferences. Values are", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 633, + 506, + 649 + ], + "spans": [ + { + "bbox": [ + 105, + 633, + 506, + 649 + ], + "score": 1.0, + "content": "calculated as the absolute difference in preferences between the two pots; 1 indicates that the player", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 645, + 487, + 659 + ], + "spans": [ + { + "bbox": [ + 106, + 645, + 487, + 659 + ], + "score": 1.0, + "content": "only uses one of the two available pots, while 0 indicates that the player uses both pots equally.", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 27 + }, + { + "type": "title", + "bbox": [ + 107, + 665, + 407, + 677 + ], + "lines": [ + { + "bbox": [ + 106, + 664, + 407, + 678 + ], + "spans": [ + { + "bbox": [ + 106, + 664, + 407, + 678 + ], + "score": 1.0, + "content": "Finding 1: FCP exhibits the best movement coordination with humans", + "type": "text" + } + ], + "index": 30 + } + ], + "index": 30 + }, + { + "type": "text", + "bbox": [ + 107, + 678, + 506, + 722 + ], + "lines": [ + { + "bbox": [ + 105, + 677, + 506, + 691 + ], + "spans": [ + { + "bbox": [ + 105, + 677, + 405, + 691 + ], + "score": 1.0, + "content": "First, we investigate how much each player moves in an episode (Figure", + "type": "text" + }, + { + "bbox": [ + 406, + 677, + 421, + 691 + ], + "score": 0.27, + "content": "\\textcircled { 8 \\mathrm { a } }", + "type": "inline_equation" + }, + { + "bbox": [ + 421, + 677, + 506, + 691 + ], + "score": 1.0, + "content": ", where moving in a", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 688, + 507, + 701 + ], + "spans": [ + { + "bbox": [ + 105, + 688, + 507, + 701 + ], + "score": 1.0, + "content": "higher fraction of timesteps may suggest fewer collisions and thus better coordination with a partner.", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "score": 1.0, + "content": "Notably, we observe two results: (1) humans rarely move, a behavior which is out-of-distribution", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 711, + 506, + 722 + ], + "spans": [ + { + "bbox": [ + 105, + 711, + 506, + 722 + ], + "score": 1.0, + "content": "for typical training methods (e.g. SP, PP) but is seen in the training distribution for BCP and FCP.", + "type": "text" + } + ], + "index": 34 + } + ], + "index": 32.5 + } + ], + "page_idx": 7, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 302, + 741, + 308, + 750 + ], + "lines": [ + { + "bbox": [ + 300, + 740, + 309, + 752 + ], + "spans": [ + { + "bbox": [ + 300, + 740, + 309, + 752 + ], + "score": 1.0, + "content": "", + "type": "text", + "height": 12, + "width": 9 + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "text", + "bbox": [ + 106, + 72, + 505, + 139 + ], + "lines": [ + { + "bbox": [ + 106, + 73, + 505, + 84 + ], + "spans": [ + { + "bbox": [ + 106, + 73, + 505, + 84 + ], + "score": 1.0, + "content": "the FCP-human teams significantly outperform all other agent-human teams, achieving the highest", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 106, + 83, + 506, + 97 + ], + "spans": [ + { + "bbox": [ + 106, + 83, + 244, + 97 + ], + "score": 1.0, + "content": "average scores across maps, every", + "type": "text" + }, + { + "bbox": [ + 244, + 84, + 286, + 95 + ], + "score": 0.9, + "content": "p < 0 . 0 0 1", + "type": "inline_equation" + }, + { + "bbox": [ + 287, + 83, + 318, + 97 + ], + "score": 1.0, + "content": "(Figure", + "type": "text" + }, + { + "bbox": [ + 318, + 83, + 333, + 95 + ], + "score": 0.81, + "content": "\\lvert \\overline { { 7 \\mathrm { a } } } \\rvert", + "type": "inline_equation" + }, + { + "bbox": [ + 333, + 83, + 506, + 97 + ], + "score": 1.0, + "content": ", while performing as well as or better than", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 106, + 94, + 505, + 107 + ], + "spans": [ + { + "bbox": [ + 106, + 94, + 319, + 107 + ], + "score": 1.0, + "content": "the other teams on each individual map (see Appendix", + "type": "text" + }, + { + "bbox": [ + 319, + 95, + 339, + 107 + ], + "score": 0.6, + "content": "\\mathbf { D } . 3 )", + "type": "inline_equation" + }, + { + "bbox": [ + 339, + 94, + 505, + 107 + ], + "score": 1.0, + "content": ". 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Similar to Carroll et al.", + "type": "text" + }, + { + "bbox": [ + 375, + 116, + 392, + 128 + ], + "score": 0.58, + "content": "\\mathbb { \\lVert \\rVert }", + "type": "inline_equation" + }, + { + "bbox": [ + 392, + 116, + 506, + 128 + ], + "score": 1.0, + "content": ", we find that BCP outscores", + "type": "text" + } + ], + "index": 4 + }, + { + "bbox": [ + 105, + 126, + 329, + 140 + ], + "spans": [ + { + "bbox": [ + 105, + 126, + 282, + 140 + ], + "score": 1.0, + "content": "SP when collaborating with human players,", + "type": "text" + }, + { + "bbox": [ + 283, + 128, + 325, + 139 + ], + "score": 0.9, + "content": "p < 0 . 0 0 1", + "type": "inline_equation" + }, + { + "bbox": [ + 325, + 126, + 329, + 140 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 5 + } + ], + "index": 2.5, + "bbox_fs": [ + 105, + 73, + 506, + 140 + ] + }, + { + "type": "title", + "bbox": [ + 106, + 150, + 333, + 162 + ], + "lines": [ + { + "bbox": [ + 106, + 149, + 333, + 164 + ], + "spans": [ + { + "bbox": [ + 106, + 149, + 333, + 164 + ], + "score": 1.0, + "content": "Finding 2: Participants prefer FCP over all baselines", + "type": "text" + } + ], + "index": 6 + } + ], + "index": 6 + }, + { + "type": "text", + "bbox": [ + 106, + 163, + 506, + 218 + ], + "lines": [ + { + "bbox": [ + 105, + 162, + 507, + 177 + ], + "spans": [ + { + "bbox": [ + 105, + 162, + 507, + 177 + ], + "score": 1.0, + "content": "FCP’s strong collaborative performance carries over to our participants’ subjective partner preferences.", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 173, + 506, + 187 + ], + "spans": [ + { + "bbox": [ + 105, + 173, + 506, + 187 + ], + "score": 1.0, + "content": "Participants expressed a significant preference for FCP partners over all other agents, including", + "type": "text" + } + ], + "index": 8 + }, + { + "bbox": [ + 105, + 183, + 506, + 198 + ], + "spans": [ + { + "bbox": [ + 105, + 183, + 176, + 198 + ], + "score": 1.0, + "content": "BCP, with every", + "type": "text" + }, + { + "bbox": [ + 176, + 185, + 215, + 196 + ], + "score": 0.9, + "content": "p < 0 . 0 5", + "type": "inline_equation" + }, + { + "bbox": [ + 215, + 183, + 247, + 198 + ], + "score": 1.0, + "content": "(Figure", + "type": "text" + }, + { + "bbox": [ + 248, + 184, + 263, + 197 + ], + "score": 0.83, + "content": "\\bar { 7 \\mathrm { c } } )", + "type": "inline_equation" + }, + { + "bbox": [ + 263, + 183, + 506, + 198 + ], + "score": 1.0, + "content": ". Notably, while human-BCP and human-PP teams did not", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 105, + 195, + 506, + 209 + ], + "spans": [ + { + "bbox": [ + 105, + 195, + 506, + 209 + ], + "score": 1.0, + "content": "significantly differ in their completed deliveries, participants reported significantly preferring BCP", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 106, + 206, + 425, + 219 + ], + "spans": [ + { + "bbox": [ + 106, + 206, + 141, + 219 + ], + "score": 1.0, + "content": "over PP,", + "type": "text" + }, + { + "bbox": [ + 141, + 207, + 183, + 218 + ], + "score": 0.9, + "content": "p = 0 . 0 0 3", + "type": "inline_equation" + }, + { + "bbox": [ + 184, + 206, + 425, + 219 + ], + "score": 1.0, + "content": ", highlighting the informativeness of our subjective analysis.", + "type": "text" + } + ], + "index": 11 + } + ], + "index": 9, + "bbox_fs": [ + 105, + 162, + 507, + 219 + ] + }, + { + "type": "image", + "bbox": [ + 107, + 230, + 504, + 352 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 107, + 230, + 504, + 352 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 107, + 230, + 504, + 352 + ], + "spans": [ + { + "bbox": [ + 107, + 230, + 504, + 352 + ], + "score": 0.927, + "type": "image", + "image_path": "14e048153d10325b3617f94b393393ed3ab6cf3e38198f8791acf0fc83b051df.jpg" + } + ] + } + ], + "index": 13, + "virtual_lines": [ + { + "bbox": [ + 107, + 230, + 504, + 270.6666666666667 + ], + "spans": [], + "index": 12 + }, + { + "bbox": [ + 107, + 270.6666666666667, + 504, + 311.33333333333337 + ], + "spans": [], + "index": 13 + }, + { + "bbox": [ + 107, + 311.33333333333337, + 504, + 352.00000000000006 + ], + "spans": [], + "index": 14 + } + ] + }, + { + "type": "image_caption", + "bbox": [ + 106, + 357, + 506, + 403 + ], + "group_id": 0, + "lines": [ + { + "bbox": [ + 105, + 357, + 507, + 370 + ], + "spans": [ + { + "bbox": [ + 105, + 357, + 507, + 370 + ], + "score": 1.0, + "content": "Figure 7: Human-agent collaborative evaluation: Evaluation and preference metrics from human-", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 368, + 505, + 379 + ], + "spans": [ + { + "bbox": [ + 105, + 368, + 233, + 379 + ], + "score": 1.0, + "content": "agent play in episodes of length", + "type": "text" + }, + { + "bbox": [ + 234, + 368, + 270, + 379 + ], + "score": 0.9, + "content": "T = 3 0 0", + "type": "inline_equation" + }, + { + "bbox": [ + 271, + 368, + 358, + 379 + ], + "score": 1.0, + "content": ". Error bars represents", + "type": "text" + }, + { + "bbox": [ + 359, + 368, + 378, + 379 + ], + "score": 0.87, + "content": "9 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 379, + 368, + 505, + 379 + ], + "score": 1.0, + "content": "confidence intervals, calculated", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 379, + 506, + 392 + ], + "spans": [ + { + "bbox": [ + 105, + 379, + 506, + 392 + ], + "score": 1.0, + "content": "over episodes. Plots aggregate data across kitchen layouts; results calculated by individual layout can", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 105, + 389, + 216, + 405 + ], + "spans": [ + { + "bbox": [ + 105, + 389, + 216, + 405 + ], + "score": 1.0, + "content": "be found in Appendix D.3.", + "type": "text" + } + ], + "index": 18 + } + ], + "index": 16.5 + } + ], + "index": 14.75 + }, + { + "type": "title", + "bbox": [ + 107, + 421, + 266, + 433 + ], + "lines": [ + { + "bbox": [ + 106, + 420, + 267, + 435 + ], + "spans": [ + { + "bbox": [ + 106, + 420, + 267, + 435 + ], + "score": 1.0, + "content": "5.3 Exploratory behavioral analysis", + "type": "text" + } + ], + "index": 19 + } + ], + "index": 19 + }, + { + "type": "text", + "bbox": [ + 106, + 441, + 505, + 464 + ], + "lines": [ + { + "bbox": [ + 105, + 440, + 505, + 454 + ], + "spans": [ + { + "bbox": [ + 105, + 440, + 505, + 454 + ], + "score": 1.0, + "content": "To better understand how the human-agent scores and preferences may have arisen, here we analyze", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 106, + 452, + 437, + 465 + ], + "spans": [ + { + "bbox": [ + 106, + 452, + 437, + 465 + ], + "score": 1.0, + "content": "the resulting action trajectories of each human and agent player in our experiment.", + "type": "text" + } + ], + "index": 21 + } + ], + "index": 20.5, + "bbox_fs": [ + 105, + 440, + 505, + 465 + ] + }, + { + "type": "image", + "bbox": [ + 138, + 478, + 474, + 597 + ], + "blocks": [ + { + "type": "image_body", + "bbox": [ + 138, + 478, + 474, + 597 + ], + "group_id": 1, + "lines": [ + { + "bbox": [ + 138, + 478, + 474, + 597 + ], + "spans": [ + { + "bbox": [ + 138, + 478, + 474, + 597 + ], + "score": 0.519, + "type": "image", + "image_path": "65959c0ab23a18c0f8de72bc451d199e7d9d9cc2698e7ca2ac4f320e26dac829.jpg" + } + ] + } + ], + "index": 23, + "virtual_lines": [ + { + "bbox": [ + 138, + 478, + 474, + 517.6666666666666 + ], + "spans": [], + "index": 22 + }, + { + "bbox": [ + 138, + 517.6666666666666, + 474, + 557.3333333333333 + ], + "spans": [], + "index": 23 + }, + { + "bbox": [ + 138, + 557.3333333333333, + 474, + 596.9999999999999 + ], + "spans": [], + "index": 24 + } + ] + } + ], + "index": 23 + }, + { + "type": "text", + "bbox": [ + 106, + 601, + 506, + 657 + ], + "lines": [ + { + "bbox": [ + 105, + 601, + 507, + 614 + ], + "spans": [ + { + "bbox": [ + 105, + 601, + 412, + 614 + ], + "score": 1.0, + "content": "Figure 8: Behavioral analysis: (a) FCP is able to move most frequently", + "type": "text" + }, + { + "bbox": [ + 412, + 601, + 432, + 613 + ], + "score": 0.85, + "content": "3 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 432, + 601, + 507, + 614 + ], + "score": 1.0, + "content": "of the time), cor-", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 105, + 613, + 506, + 626 + ], + "spans": [ + { + "bbox": [ + 105, + 613, + 506, + 626 + ], + "score": 1.0, + "content": "responding to the best movement coordination with human partners. (b) FCP exhibits the most", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 105, + 624, + 506, + 636 + ], + "spans": [ + { + "bbox": [ + 105, + 624, + 506, + 636 + ], + "score": 1.0, + "content": "equal preferences over cooking pots (0.11 difference), aligning with human preferences. Values are", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 633, + 506, + 649 + ], + "spans": [ + { + "bbox": [ + 105, + 633, + 506, + 649 + ], + "score": 1.0, + "content": "calculated as the absolute difference in preferences between the two pots; 1 indicates that the player", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 106, + 645, + 487, + 659 + ], + "spans": [ + { + "bbox": [ + 106, + 645, + 487, + 659 + ], + "score": 1.0, + "content": "only uses one of the two available pots, while 0 indicates that the player uses both pots equally.", + "type": "text" + } + ], + "index": 29 + } + ], + "index": 27, + "bbox_fs": [ + 105, + 601, + 507, + 659 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 665, + 407, + 677 + ], + "lines": [ + { + "bbox": [ + 106, + 664, + 407, + 678 + ], + "spans": [ + { + "bbox": [ + 106, + 664, + 407, + 678 + ], + "score": 1.0, + "content": "Finding 1: FCP exhibits the best movement coordination with humans", + "type": "text" + } + ], + "index": 30 + } + ], + "index": 30 + }, + { + "type": "text", + "bbox": [ + 107, + 678, + 506, + 722 + ], + "lines": [ + { + "bbox": [ + 105, + 677, + 506, + 691 + ], + "spans": [ + { + "bbox": [ + 105, + 677, + 405, + 691 + ], + "score": 1.0, + "content": "First, we investigate how much each player moves in an episode (Figure", + "type": "text" + }, + { + "bbox": [ + 406, + 677, + 421, + 691 + ], + "score": 0.27, + "content": "\\textcircled { 8 \\mathrm { a } }", + "type": "inline_equation" + }, + { + "bbox": [ + 421, + 677, + 506, + 691 + ], + "score": 1.0, + "content": ", where moving in a", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 688, + 507, + 701 + ], + "spans": [ + { + "bbox": [ + 105, + 688, + 507, + 701 + ], + "score": 1.0, + "content": "higher fraction of timesteps may suggest fewer collisions and thus better coordination with a partner.", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "score": 1.0, + "content": "Notably, we observe two results: (1) humans rarely move, a behavior which is out-of-distribution", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 711, + 506, + 722 + ], + "spans": [ + { + "bbox": [ + 105, + 711, + 506, + 722 + ], + "score": 1.0, + "content": "for typical training methods (e.g. SP, PP) but is seen in the training distribution for BCP and FCP.", + "type": "text" + } + ], + "index": 34 + } + ], + "index": 32.5, + "bbox_fs": [ + 105, + 677, + 507, + 722 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "text", + "bbox": [ + 106, + 73, + 505, + 117 + ], + "lines": [ + { + "bbox": [ + 105, + 73, + 505, + 86 + ], + "spans": [ + { + "bbox": [ + 105, + 73, + 505, + 86 + ], + "score": 1.0, + "content": "(2) FCP moves the most on all layouts other than Forced, suggesting it is better at coordinating its", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 83, + 506, + 96 + ], + "spans": [ + { + "bbox": [ + 105, + 83, + 506, + 96 + ], + "score": 1.0, + "content": "movement strategy with its partner. This result was also reported by human participants, for example:", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 104, + 93, + 506, + 107 + ], + "spans": [ + { + "bbox": [ + 104, + 93, + 506, + 107 + ], + "score": 1.0, + "content": "“I noticed that some of my partners seemed to know they needed to move around me, while others", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 106, + 473, + 119 + ], + "spans": [ + { + "bbox": [ + 105, + 106, + 473, + 119 + ], + "score": 1.0, + "content": "seemed to get ‘stuck’ until I moved out of their way” (see Appendix D for more examples).", + "type": "text" + } + ], + "index": 3 + } + ], + "index": 1.5 + }, + { + "type": "title", + "bbox": [ + 106, + 129, + 444, + 141 + ], + "lines": [ + { + "bbox": [ + 106, + 129, + 446, + 143 + ], + "spans": [ + { + "bbox": [ + 106, + 129, + 446, + 143 + ], + "score": 1.0, + "content": "Finding 2: FCP’s preferences over cooking pots aligns best with that of humans", + "type": "text" + } + ], + "index": 4 + } + ], + "index": 4 + }, + { + "type": "text", + "bbox": [ + 107, + 143, + 505, + 187 + ], + "lines": [ + { + "bbox": [ + 106, + 143, + 505, + 155 + ], + "spans": [ + { + "bbox": [ + 106, + 143, + 505, + 155 + ], + "score": 1.0, + "content": "Next, we investigate whether there was a preference for a specific cooking pot in the layouts which", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 106, + 153, + 506, + 167 + ], + "spans": [ + { + "bbox": [ + 106, + 153, + 243, + 167 + ], + "score": 1.0, + "content": "included two cooking pots (Figure", + "type": "text" + }, + { + "bbox": [ + 243, + 154, + 258, + 167 + ], + "score": 0.55, + "content": "\\textcircled { 8 6 }", + "type": "inline_equation" + }, + { + "bbox": [ + 258, + 153, + 506, + 167 + ], + "score": 1.0, + "content": ". To do this, we calculate the difference in the number of times", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 165, + 505, + 177 + ], + "spans": [ + { + "bbox": [ + 106, + 165, + 505, + 177 + ], + "score": 1.0, + "content": "each pot was used by each player, where a high value indicates a strong preference for one pot and a", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 176, + 344, + 188 + ], + "spans": [ + { + "bbox": [ + 105, + 176, + 344, + 188 + ], + "score": 1.0, + "content": "low value indicates more equal preference for the two pots.", + "type": "text" + } + ], + "index": 8 + } + ], + "index": 6.5 + }, + { + "type": "text", + "bbox": [ + 107, + 192, + 505, + 247 + ], + "lines": [ + { + "bbox": [ + 105, + 191, + 505, + 204 + ], + "spans": [ + { + "bbox": [ + 105, + 191, + 505, + 204 + ], + "score": 1.0, + "content": "As can be seen in the FCP column, our agent typically has the most aligned preferences with that of", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 202, + 505, + 216 + ], + "spans": [ + { + "bbox": [ + 106, + 202, + 505, + 216 + ], + "score": 1.0, + "content": "humans (0.11 for FCP to 0.14 for humans). Behaviorally speaking, this means that our agent prefers", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 213, + 506, + 227 + ], + "spans": [ + { + "bbox": [ + 105, + 213, + 230, + 227 + ], + "score": 1.0, + "content": "one cooking pot over the other", + "type": "text" + }, + { + "bbox": [ + 231, + 214, + 258, + 225 + ], + "score": 0.88, + "content": "5 5 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 259, + 213, + 506, + 227 + ], + "score": 1.0, + "content": "of the time (i.e. a 0.11 point difference). In contrast, all other", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 225, + 506, + 238 + ], + "spans": [ + { + "bbox": [ + 105, + 225, + 506, + 238 + ], + "score": 1.0, + "content": "agents have a strong preference for a single pot. This is a non-adaptive strategy which generalizes", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 235, + 442, + 249 + ], + "spans": [ + { + "bbox": [ + 105, + 235, + 442, + 249 + ], + "score": 1.0, + "content": "poorly to typical human behavior of using both pots, leading to worse performance.", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 11 + }, + { + "type": "title", + "bbox": [ + 107, + 259, + 180, + 273 + ], + "lines": [ + { + "bbox": [ + 104, + 258, + 181, + 275 + ], + "spans": [ + { + "bbox": [ + 104, + 258, + 181, + 275 + ], + "score": 1.0, + "content": "6 Discussion", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 14 + }, + { + "type": "text", + "bbox": [ + 106, + 279, + 505, + 356 + ], + "lines": [ + { + "bbox": [ + 106, + 280, + 505, + 292 + ], + "spans": [ + { + "bbox": [ + 106, + 280, + 505, + 292 + ], + "score": 1.0, + "content": "Summary In this work, we investigated the challenging problem of zero-shot collaboration with", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 291, + 505, + 303 + ], + "spans": [ + { + "bbox": [ + 105, + 291, + 505, + 303 + ], + "score": 1.0, + "content": "humans without using human data in the training pipeline. To accomplish this, we introduced", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 301, + 506, + 315 + ], + "spans": [ + { + "bbox": [ + 105, + 301, + 506, + 315 + ], + "score": 1.0, + "content": "Fictitious Co-Play (FCP) – a surprisingly simple yet effective method based on creating a diverse set", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 313, + 505, + 325 + ], + "spans": [ + { + "bbox": [ + 106, + 313, + 505, + 325 + ], + "score": 1.0, + "content": "of training partners. We found that FCP agents scored significantly higher than all baselines when", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 323, + 506, + 336 + ], + "spans": [ + { + "bbox": [ + 105, + 323, + 506, + 336 + ], + "score": 1.0, + "content": "partnered with both novel agent and human partners. Furthermore, through a rigorous human-agent", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 334, + 505, + 348 + ], + "spans": [ + { + "bbox": [ + 105, + 334, + 505, + 348 + ], + "score": 1.0, + "content": "experimental design, we also found that humans reported a strong subjective preference to partnering", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 106, + 345, + 249, + 358 + ], + "spans": [ + { + "bbox": [ + 106, + 345, + 249, + 358 + ], + "score": 1.0, + "content": "with FCP agents over all baselines.", + "type": "text" + } + ], + "index": 21 + } + ], + "index": 18 + }, + { + "type": "text", + "bbox": [ + 107, + 361, + 505, + 460 + ], + "lines": [ + { + "bbox": [ + 105, + 361, + 505, + 374 + ], + "spans": [ + { + "bbox": [ + 105, + 361, + 505, + 374 + ], + "score": 1.0, + "content": "Limitations and future work Our method currently relies on the manual process of initially", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 373, + 505, + 385 + ], + "spans": [ + { + "bbox": [ + 106, + 373, + 505, + 385 + ], + "score": 1.0, + "content": "training and selecting a diverse set of partners. This is not only time consuming, but also prone to", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 383, + 505, + 396 + ], + "spans": [ + { + "bbox": [ + 105, + 383, + 505, + 396 + ], + "score": 1.0, + "content": "researcher biases that may negatively influence the behavior of the created agents. Additionally, while", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 104, + 393, + 507, + 408 + ], + "spans": [ + { + "bbox": [ + 104, + 393, + 302, + 408 + ], + "score": 1.0, + "content": "we found FCP with a partner population size of", + "type": "text" + }, + { + "bbox": [ + 302, + 394, + 336, + 405 + ], + "score": 0.9, + "content": "N = 3 2", + "type": "inline_equation" + }, + { + "bbox": [ + 336, + 393, + 507, + 408 + ], + "score": 1.0, + "content": "sufficient here, for more complex games,", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 106, + 405, + 505, + 418 + ], + "spans": [ + { + "bbox": [ + 106, + 405, + 505, + 418 + ], + "score": 1.0, + "content": "FCP may require an unrealistically large partner population size to represent sufficiently diverse", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 416, + 504, + 428 + ], + "spans": [ + { + "bbox": [ + 106, + 416, + 504, + 428 + ], + "score": 1.0, + "content": "strategies. To address these concerns, methods for automatically generating partner diversity for", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 426, + 505, + 441 + ], + "spans": [ + { + "bbox": [ + 105, + 426, + 505, + 441 + ], + "score": 1.0, + "content": "common-payoff games may be important. Possibilities include adaptive population matchmaking", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 437, + 505, + 451 + ], + "spans": [ + { + "bbox": [ + 105, + 437, + 295, + 451 + ], + "score": 1.0, + "content": "as been used in competitive zero-sum games", + "type": "text" + }, + { + "bbox": [ + 295, + 437, + 313, + 449 + ], + "score": 0.8, + "content": "\\mathbb { \\lVert 6 9 \\rVert }", + "type": "inline_equation" + }, + { + "bbox": [ + 313, + 437, + 505, + 451 + ], + "score": 1.0, + "content": ", as well as auxiliary objectives that explicitly", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 448, + 281, + 461 + ], + "spans": [ + { + "bbox": [ + 105, + 448, + 281, + 461 + ], + "score": 1.0, + "content": "encourage behavioral diversity [19, 45, 46].", + "type": "text" + } + ], + "index": 30 + } + ], + "index": 26 + }, + { + "type": "text", + "bbox": [ + 107, + 465, + 505, + 564 + ], + "lines": [ + { + "bbox": [ + 107, + 466, + 505, + 476 + ], + "spans": [ + { + "bbox": [ + 107, + 466, + 505, + 476 + ], + "score": 1.0, + "content": "Our method requires a known and fixed reward function. We also focus on one domain in order to", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 476, + 506, + 489 + ], + "spans": [ + { + "bbox": [ + 105, + 476, + 506, + 489 + ], + "score": 1.0, + "content": "compare with prior work which has argued that human-in-the-loop training is necessary. Consequently,", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 488, + 506, + 499 + ], + "spans": [ + { + "bbox": [ + 105, + 488, + 506, + 499 + ], + "score": 1.0, + "content": "the resulting agents are only designed to adaptively collaborate on a single task, and not to infer", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 497, + 506, + 511 + ], + "spans": [ + { + "bbox": [ + 105, + 497, + 230, + 511 + ], + "score": 1.0, + "content": "human preferences in general", + "type": "text" + }, + { + "bbox": [ + 231, + 497, + 273, + 509 + ], + "score": 0.46, + "content": "\\textcircled { 1 1 } \\textcircled { 3 3 } \\textcircled { 5 9 }", + "type": "inline_equation" + }, + { + "bbox": [ + 274, + 497, + 506, + 511 + ], + "score": 1.0, + "content": ". Moreover, if a task’s reward function is poorly aligned", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 508, + 506, + 521 + ], + "spans": [ + { + "bbox": [ + 105, + 508, + 506, + 521 + ], + "score": 1.0, + "content": "with how humans approach the task, our method may well produce subpar partners, as would", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 520, + 505, + 531 + ], + "spans": [ + { + "bbox": [ + 105, + 520, + 505, + 531 + ], + "score": 1.0, + "content": "any method without access to human data. Thus, additional domains and tasks should be studied", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 530, + 506, + 543 + ], + "spans": [ + { + "bbox": [ + 105, + 530, + 506, + 543 + ], + "score": 1.0, + "content": "to better understand how our method generalizes. Targeted experiments to test specific forms of", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 541, + 504, + 554 + ], + "spans": [ + { + "bbox": [ + 105, + 542, + 328, + 554 + ], + "score": 1.0, + "content": "generalization may be especially helpful in this regard", + "type": "text" + }, + { + "bbox": [ + 329, + 541, + 346, + 552 + ], + "score": 0.76, + "content": "\\overline { { \\| 3 8 \\| } }", + "type": "inline_equation" + }, + { + "bbox": [ + 346, + 542, + 504, + 554 + ], + "score": 1.0, + "content": ", as could approaches that procedurally", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 552, + 352, + 565 + ], + "spans": [ + { + "bbox": [ + 105, + 553, + 335, + 565 + ], + "score": 1.0, + "content": "generate environment layouts requiring diverse solutions", + "type": "text" + }, + { + "bbox": [ + 335, + 552, + 352, + 564 + ], + "score": 0.39, + "content": "\\lVert 2 2 \\rVert", + "type": "inline_equation" + } + ], + "index": 39 + } + ], + "index": 35 + }, + { + "type": "text", + "bbox": [ + 107, + 569, + 505, + 613 + ], + "lines": [ + { + "bbox": [ + 105, + 569, + 506, + 581 + ], + "spans": [ + { + "bbox": [ + 105, + 569, + 506, + 581 + ], + "score": 1.0, + "content": "Finally, it may be possible to produce even stronger agent assistants by combining the strengths of", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 579, + 505, + 592 + ], + "spans": [ + { + "bbox": [ + 105, + 579, + 395, + 592 + ], + "score": 1.0, + "content": "FCP (i.e. diversity) and BCP (i.e. human-like play). Indeed, Knott et al.", + "type": "text" + }, + { + "bbox": [ + 396, + 579, + 413, + 591 + ], + "score": 0.5, + "content": "\\pmb { \\Vert 3 8 \\Vert }", + "type": "inline_equation" + }, + { + "bbox": [ + 414, + 579, + 505, + 592 + ], + "score": 1.0, + "content": "recently demonstrated", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 590, + 506, + 604 + ], + "spans": [ + { + "bbox": [ + 105, + 590, + 506, + 604 + ], + "score": 1.0, + "content": "that modifying BCP to train with multiple BC partners produces more robust collaboration with", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 602, + 425, + 614 + ], + "spans": [ + { + "bbox": [ + 105, + 602, + 425, + 614 + ], + "score": 1.0, + "content": "held-out agents, a finding that would be interesting to test with human partners.", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 41.5 + }, + { + "type": "text", + "bbox": [ + 107, + 617, + 505, + 684 + ], + "lines": [ + { + "bbox": [ + 105, + 617, + 505, + 630 + ], + "spans": [ + { + "bbox": [ + 105, + 617, + 505, + 630 + ], + "score": 1.0, + "content": "Societal impact A challenge for this line of work is ensuring agent behavior is aligned with human", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 627, + 506, + 642 + ], + "spans": [ + { + "bbox": [ + 105, + 627, + 506, + 642 + ], + "score": 1.0, + "content": "values (i.e. the AI value alignment problem [23, 59]). Our method has no guarantees that the resulting", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 640, + 505, + 652 + ], + "spans": [ + { + "bbox": [ + 105, + 640, + 505, + 652 + ], + "score": 1.0, + "content": "policy aligns with the preferences, intentions, or welfare of its potential partners. It likewise does", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 106, + 651, + 505, + 663 + ], + "spans": [ + { + "bbox": [ + 106, + 651, + 505, + 663 + ], + "score": 1.0, + "content": "not exclude the possibility that the target being optimized for is harmful (e.g. if the agent’s partner", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 662, + 505, + 674 + ], + "spans": [ + { + "bbox": [ + 105, + 662, + 505, + 674 + ], + "score": 1.0, + "content": "expresses preferences or intentions to harm others). This could therefore produce negative societal", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 105, + 672, + 486, + 685 + ], + "spans": [ + { + "bbox": [ + 105, + 672, + 486, + 685 + ], + "score": 1.0, + "content": "effects either if training leads to poor alignment or if agents are optimized for harmful metrics.", + "type": "text" + } + ], + "index": 49 + } + ], + "index": 46.5 + }, + { + "type": "text", + "bbox": [ + 108, + 689, + 504, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 689, + 505, + 701 + ], + "spans": [ + { + "bbox": [ + 106, + 689, + 505, + 701 + ], + "score": 1.0, + "content": "One potential strategy for mitigating these risks is the use of human preference data [15]. Such data", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "score": 1.0, + "content": "could be used to fine-tune and filter trained agents before deployment, encouraging better alignment", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 106, + 711, + 505, + 723 + ], + "spans": [ + { + "bbox": [ + 106, + 711, + 505, + 723 + ], + "score": 1.0, + "content": "with human values. A key question in this line of research is how human preference data should be", + "type": "text" + } + ], + "index": 52 + } + ], + "index": 51 + } + ], + "page_idx": 8, + "page_size": [ + 612, + 792 + ], + "discarded_blocks": [ + { + "type": "discarded", + "bbox": [ + 302, + 742, + 308, + 750 + ], + "lines": [ + { + "bbox": [ + 302, + 741, + 309, + 752 + ], + "spans": [ + { + "bbox": [ + 302, + 741, + 309, + 752 + ], + "score": 1.0, + "content": "9", + "type": "text" + } + ] + } + ] + } + ], + "para_blocks": [ + { + "type": "text", + "bbox": [ + 106, + 73, + 505, + 117 + ], + "lines": [ + { + "bbox": [ + 105, + 73, + 505, + 86 + ], + "spans": [ + { + "bbox": [ + 105, + 73, + 505, + 86 + ], + "score": 1.0, + "content": "(2) FCP moves the most on all layouts other than Forced, suggesting it is better at coordinating its", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 83, + 506, + 96 + ], + "spans": [ + { + "bbox": [ + 105, + 83, + 506, + 96 + ], + "score": 1.0, + "content": "movement strategy with its partner. This result was also reported by human participants, for example:", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 104, + 93, + 506, + 107 + ], + "spans": [ + { + "bbox": [ + 104, + 93, + 506, + 107 + ], + "score": 1.0, + "content": "“I noticed that some of my partners seemed to know they needed to move around me, while others", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 105, + 106, + 473, + 119 + ], + "spans": [ + { + "bbox": [ + 105, + 106, + 473, + 119 + ], + "score": 1.0, + "content": "seemed to get ‘stuck’ until I moved out of their way” (see Appendix D for more examples).", + "type": "text" + } + ], + "index": 3 + } + ], + "index": 1.5, + "bbox_fs": [ + 104, + 73, + 506, + 119 + ] + }, + { + "type": "title", + "bbox": [ + 106, + 129, + 444, + 141 + ], + "lines": [ + { + "bbox": [ + 106, + 129, + 446, + 143 + ], + "spans": [ + { + "bbox": [ + 106, + 129, + 446, + 143 + ], + "score": 1.0, + "content": "Finding 2: FCP’s preferences over cooking pots aligns best with that of humans", + "type": "text" + } + ], + "index": 4 + } + ], + "index": 4 + }, + { + "type": "text", + "bbox": [ + 107, + 143, + 505, + 187 + ], + "lines": [ + { + "bbox": [ + 106, + 143, + 505, + 155 + ], + "spans": [ + { + "bbox": [ + 106, + 143, + 505, + 155 + ], + "score": 1.0, + "content": "Next, we investigate whether there was a preference for a specific cooking pot in the layouts which", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 106, + 153, + 506, + 167 + ], + "spans": [ + { + "bbox": [ + 106, + 153, + 243, + 167 + ], + "score": 1.0, + "content": "included two cooking pots (Figure", + "type": "text" + }, + { + "bbox": [ + 243, + 154, + 258, + 167 + ], + "score": 0.55, + "content": "\\textcircled { 8 6 }", + "type": "inline_equation" + }, + { + "bbox": [ + 258, + 153, + 506, + 167 + ], + "score": 1.0, + "content": ". To do this, we calculate the difference in the number of times", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 106, + 165, + 505, + 177 + ], + "spans": [ + { + "bbox": [ + 106, + 165, + 505, + 177 + ], + "score": 1.0, + "content": "each pot was used by each player, where a high value indicates a strong preference for one pot and a", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 176, + 344, + 188 + ], + "spans": [ + { + "bbox": [ + 105, + 176, + 344, + 188 + ], + "score": 1.0, + "content": "low value indicates more equal preference for the two pots.", + "type": "text" + } + ], + "index": 8 + } + ], + "index": 6.5, + "bbox_fs": [ + 105, + 143, + 506, + 188 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 192, + 505, + 247 + ], + "lines": [ + { + "bbox": [ + 105, + 191, + 505, + 204 + ], + "spans": [ + { + "bbox": [ + 105, + 191, + 505, + 204 + ], + "score": 1.0, + "content": "As can be seen in the FCP column, our agent typically has the most aligned preferences with that of", + "type": "text" + } + ], + "index": 9 + }, + { + "bbox": [ + 106, + 202, + 505, + 216 + ], + "spans": [ + { + "bbox": [ + 106, + 202, + 505, + 216 + ], + "score": 1.0, + "content": "humans (0.11 for FCP to 0.14 for humans). Behaviorally speaking, this means that our agent prefers", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 105, + 213, + 506, + 227 + ], + "spans": [ + { + "bbox": [ + 105, + 213, + 230, + 227 + ], + "score": 1.0, + "content": "one cooking pot over the other", + "type": "text" + }, + { + "bbox": [ + 231, + 214, + 258, + 225 + ], + "score": 0.88, + "content": "5 5 . 5 \\%", + "type": "inline_equation" + }, + { + "bbox": [ + 259, + 213, + 506, + 227 + ], + "score": 1.0, + "content": "of the time (i.e. a 0.11 point difference). In contrast, all other", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 225, + 506, + 238 + ], + "spans": [ + { + "bbox": [ + 105, + 225, + 506, + 238 + ], + "score": 1.0, + "content": "agents have a strong preference for a single pot. This is a non-adaptive strategy which generalizes", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 235, + 442, + 249 + ], + "spans": [ + { + "bbox": [ + 105, + 235, + 442, + 249 + ], + "score": 1.0, + "content": "poorly to typical human behavior of using both pots, leading to worse performance.", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 11, + "bbox_fs": [ + 105, + 191, + 506, + 249 + ] + }, + { + "type": "title", + "bbox": [ + 107, + 259, + 180, + 273 + ], + "lines": [ + { + "bbox": [ + 104, + 258, + 181, + 275 + ], + "spans": [ + { + "bbox": [ + 104, + 258, + 181, + 275 + ], + "score": 1.0, + "content": "6 Discussion", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 14 + }, + { + "type": "text", + "bbox": [ + 106, + 279, + 505, + 356 + ], + "lines": [ + { + "bbox": [ + 106, + 280, + 505, + 292 + ], + "spans": [ + { + "bbox": [ + 106, + 280, + 505, + 292 + ], + "score": 1.0, + "content": "Summary In this work, we investigated the challenging problem of zero-shot collaboration with", + "type": "text" + } + ], + "index": 15 + }, + { + "bbox": [ + 105, + 291, + 505, + 303 + ], + "spans": [ + { + "bbox": [ + 105, + 291, + 505, + 303 + ], + "score": 1.0, + "content": "humans without using human data in the training pipeline. To accomplish this, we introduced", + "type": "text" + } + ], + "index": 16 + }, + { + "bbox": [ + 105, + 301, + 506, + 315 + ], + "spans": [ + { + "bbox": [ + 105, + 301, + 506, + 315 + ], + "score": 1.0, + "content": "Fictitious Co-Play (FCP) – a surprisingly simple yet effective method based on creating a diverse set", + "type": "text" + } + ], + "index": 17 + }, + { + "bbox": [ + 106, + 313, + 505, + 325 + ], + "spans": [ + { + "bbox": [ + 106, + 313, + 505, + 325 + ], + "score": 1.0, + "content": "of training partners. We found that FCP agents scored significantly higher than all baselines when", + "type": "text" + } + ], + "index": 18 + }, + { + "bbox": [ + 105, + 323, + 506, + 336 + ], + "spans": [ + { + "bbox": [ + 105, + 323, + 506, + 336 + ], + "score": 1.0, + "content": "partnered with both novel agent and human partners. Furthermore, through a rigorous human-agent", + "type": "text" + } + ], + "index": 19 + }, + { + "bbox": [ + 105, + 334, + 505, + 348 + ], + "spans": [ + { + "bbox": [ + 105, + 334, + 505, + 348 + ], + "score": 1.0, + "content": "experimental design, we also found that humans reported a strong subjective preference to partnering", + "type": "text" + } + ], + "index": 20 + }, + { + "bbox": [ + 106, + 345, + 249, + 358 + ], + "spans": [ + { + "bbox": [ + 106, + 345, + 249, + 358 + ], + "score": 1.0, + "content": "with FCP agents over all baselines.", + "type": "text" + } + ], + "index": 21 + } + ], + "index": 18, + "bbox_fs": [ + 105, + 280, + 506, + 358 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 361, + 505, + 460 + ], + "lines": [ + { + "bbox": [ + 105, + 361, + 505, + 374 + ], + "spans": [ + { + "bbox": [ + 105, + 361, + 505, + 374 + ], + "score": 1.0, + "content": "Limitations and future work Our method currently relies on the manual process of initially", + "type": "text" + } + ], + "index": 22 + }, + { + "bbox": [ + 106, + 373, + 505, + 385 + ], + "spans": [ + { + "bbox": [ + 106, + 373, + 505, + 385 + ], + "score": 1.0, + "content": "training and selecting a diverse set of partners. This is not only time consuming, but also prone to", + "type": "text" + } + ], + "index": 23 + }, + { + "bbox": [ + 105, + 383, + 505, + 396 + ], + "spans": [ + { + "bbox": [ + 105, + 383, + 505, + 396 + ], + "score": 1.0, + "content": "researcher biases that may negatively influence the behavior of the created agents. Additionally, while", + "type": "text" + } + ], + "index": 24 + }, + { + "bbox": [ + 104, + 393, + 507, + 408 + ], + "spans": [ + { + "bbox": [ + 104, + 393, + 302, + 408 + ], + "score": 1.0, + "content": "we found FCP with a partner population size of", + "type": "text" + }, + { + "bbox": [ + 302, + 394, + 336, + 405 + ], + "score": 0.9, + "content": "N = 3 2", + "type": "inline_equation" + }, + { + "bbox": [ + 336, + 393, + 507, + 408 + ], + "score": 1.0, + "content": "sufficient here, for more complex games,", + "type": "text" + } + ], + "index": 25 + }, + { + "bbox": [ + 106, + 405, + 505, + 418 + ], + "spans": [ + { + "bbox": [ + 106, + 405, + 505, + 418 + ], + "score": 1.0, + "content": "FCP may require an unrealistically large partner population size to represent sufficiently diverse", + "type": "text" + } + ], + "index": 26 + }, + { + "bbox": [ + 106, + 416, + 504, + 428 + ], + "spans": [ + { + "bbox": [ + 106, + 416, + 504, + 428 + ], + "score": 1.0, + "content": "strategies. To address these concerns, methods for automatically generating partner diversity for", + "type": "text" + } + ], + "index": 27 + }, + { + "bbox": [ + 105, + 426, + 505, + 441 + ], + "spans": [ + { + "bbox": [ + 105, + 426, + 505, + 441 + ], + "score": 1.0, + "content": "common-payoff games may be important. Possibilities include adaptive population matchmaking", + "type": "text" + } + ], + "index": 28 + }, + { + "bbox": [ + 105, + 437, + 505, + 451 + ], + "spans": [ + { + "bbox": [ + 105, + 437, + 295, + 451 + ], + "score": 1.0, + "content": "as been used in competitive zero-sum games", + "type": "text" + }, + { + "bbox": [ + 295, + 437, + 313, + 449 + ], + "score": 0.8, + "content": "\\mathbb { \\lVert 6 9 \\rVert }", + "type": "inline_equation" + }, + { + "bbox": [ + 313, + 437, + 505, + 451 + ], + "score": 1.0, + "content": ", as well as auxiliary objectives that explicitly", + "type": "text" + } + ], + "index": 29 + }, + { + "bbox": [ + 105, + 448, + 281, + 461 + ], + "spans": [ + { + "bbox": [ + 105, + 448, + 281, + 461 + ], + "score": 1.0, + "content": "encourage behavioral diversity [19, 45, 46].", + "type": "text" + } + ], + "index": 30 + } + ], + "index": 26, + "bbox_fs": [ + 104, + 361, + 507, + 461 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 465, + 505, + 564 + ], + "lines": [ + { + "bbox": [ + 107, + 466, + 505, + 476 + ], + "spans": [ + { + "bbox": [ + 107, + 466, + 505, + 476 + ], + "score": 1.0, + "content": "Our method requires a known and fixed reward function. We also focus on one domain in order to", + "type": "text" + } + ], + "index": 31 + }, + { + "bbox": [ + 105, + 476, + 506, + 489 + ], + "spans": [ + { + "bbox": [ + 105, + 476, + 506, + 489 + ], + "score": 1.0, + "content": "compare with prior work which has argued that human-in-the-loop training is necessary. Consequently,", + "type": "text" + } + ], + "index": 32 + }, + { + "bbox": [ + 105, + 488, + 506, + 499 + ], + "spans": [ + { + "bbox": [ + 105, + 488, + 506, + 499 + ], + "score": 1.0, + "content": "the resulting agents are only designed to adaptively collaborate on a single task, and not to infer", + "type": "text" + } + ], + "index": 33 + }, + { + "bbox": [ + 105, + 497, + 506, + 511 + ], + "spans": [ + { + "bbox": [ + 105, + 497, + 230, + 511 + ], + "score": 1.0, + "content": "human preferences in general", + "type": "text" + }, + { + "bbox": [ + 231, + 497, + 273, + 509 + ], + "score": 0.46, + "content": "\\textcircled { 1 1 } \\textcircled { 3 3 } \\textcircled { 5 9 }", + "type": "inline_equation" + }, + { + "bbox": [ + 274, + 497, + 506, + 511 + ], + "score": 1.0, + "content": ". Moreover, if a task’s reward function is poorly aligned", + "type": "text" + } + ], + "index": 34 + }, + { + "bbox": [ + 105, + 508, + 506, + 521 + ], + "spans": [ + { + "bbox": [ + 105, + 508, + 506, + 521 + ], + "score": 1.0, + "content": "with how humans approach the task, our method may well produce subpar partners, as would", + "type": "text" + } + ], + "index": 35 + }, + { + "bbox": [ + 105, + 520, + 505, + 531 + ], + "spans": [ + { + "bbox": [ + 105, + 520, + 505, + 531 + ], + "score": 1.0, + "content": "any method without access to human data. Thus, additional domains and tasks should be studied", + "type": "text" + } + ], + "index": 36 + }, + { + "bbox": [ + 105, + 530, + 506, + 543 + ], + "spans": [ + { + "bbox": [ + 105, + 530, + 506, + 543 + ], + "score": 1.0, + "content": "to better understand how our method generalizes. Targeted experiments to test specific forms of", + "type": "text" + } + ], + "index": 37 + }, + { + "bbox": [ + 105, + 541, + 504, + 554 + ], + "spans": [ + { + "bbox": [ + 105, + 542, + 328, + 554 + ], + "score": 1.0, + "content": "generalization may be especially helpful in this regard", + "type": "text" + }, + { + "bbox": [ + 329, + 541, + 346, + 552 + ], + "score": 0.76, + "content": "\\overline { { \\| 3 8 \\| } }", + "type": "inline_equation" + }, + { + "bbox": [ + 346, + 542, + 504, + 554 + ], + "score": 1.0, + "content": ", as could approaches that procedurally", + "type": "text" + } + ], + "index": 38 + }, + { + "bbox": [ + 105, + 552, + 352, + 565 + ], + "spans": [ + { + "bbox": [ + 105, + 553, + 335, + 565 + ], + "score": 1.0, + "content": "generate environment layouts requiring diverse solutions", + "type": "text" + }, + { + "bbox": [ + 335, + 552, + 352, + 564 + ], + "score": 0.39, + "content": "\\lVert 2 2 \\rVert", + "type": "inline_equation" + } + ], + "index": 39 + } + ], + "index": 35, + "bbox_fs": [ + 105, + 466, + 506, + 565 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 569, + 505, + 613 + ], + "lines": [ + { + "bbox": [ + 105, + 569, + 506, + 581 + ], + "spans": [ + { + "bbox": [ + 105, + 569, + 506, + 581 + ], + "score": 1.0, + "content": "Finally, it may be possible to produce even stronger agent assistants by combining the strengths of", + "type": "text" + } + ], + "index": 40 + }, + { + "bbox": [ + 105, + 579, + 505, + 592 + ], + "spans": [ + { + "bbox": [ + 105, + 579, + 395, + 592 + ], + "score": 1.0, + "content": "FCP (i.e. diversity) and BCP (i.e. human-like play). Indeed, Knott et al.", + "type": "text" + }, + { + "bbox": [ + 396, + 579, + 413, + 591 + ], + "score": 0.5, + "content": "\\pmb { \\Vert 3 8 \\Vert }", + "type": "inline_equation" + }, + { + "bbox": [ + 414, + 579, + 505, + 592 + ], + "score": 1.0, + "content": "recently demonstrated", + "type": "text" + } + ], + "index": 41 + }, + { + "bbox": [ + 105, + 590, + 506, + 604 + ], + "spans": [ + { + "bbox": [ + 105, + 590, + 506, + 604 + ], + "score": 1.0, + "content": "that modifying BCP to train with multiple BC partners produces more robust collaboration with", + "type": "text" + } + ], + "index": 42 + }, + { + "bbox": [ + 105, + 602, + 425, + 614 + ], + "spans": [ + { + "bbox": [ + 105, + 602, + 425, + 614 + ], + "score": 1.0, + "content": "held-out agents, a finding that would be interesting to test with human partners.", + "type": "text" + } + ], + "index": 43 + } + ], + "index": 41.5, + "bbox_fs": [ + 105, + 569, + 506, + 614 + ] + }, + { + "type": "text", + "bbox": [ + 107, + 617, + 505, + 684 + ], + "lines": [ + { + "bbox": [ + 105, + 617, + 505, + 630 + ], + "spans": [ + { + "bbox": [ + 105, + 617, + 505, + 630 + ], + "score": 1.0, + "content": "Societal impact A challenge for this line of work is ensuring agent behavior is aligned with human", + "type": "text" + } + ], + "index": 44 + }, + { + "bbox": [ + 105, + 627, + 506, + 642 + ], + "spans": [ + { + "bbox": [ + 105, + 627, + 506, + 642 + ], + "score": 1.0, + "content": "values (i.e. the AI value alignment problem [23, 59]). Our method has no guarantees that the resulting", + "type": "text" + } + ], + "index": 45 + }, + { + "bbox": [ + 105, + 640, + 505, + 652 + ], + "spans": [ + { + "bbox": [ + 105, + 640, + 505, + 652 + ], + "score": 1.0, + "content": "policy aligns with the preferences, intentions, or welfare of its potential partners. It likewise does", + "type": "text" + } + ], + "index": 46 + }, + { + "bbox": [ + 106, + 651, + 505, + 663 + ], + "spans": [ + { + "bbox": [ + 106, + 651, + 505, + 663 + ], + "score": 1.0, + "content": "not exclude the possibility that the target being optimized for is harmful (e.g. if the agent’s partner", + "type": "text" + } + ], + "index": 47 + }, + { + "bbox": [ + 105, + 662, + 505, + 674 + ], + "spans": [ + { + "bbox": [ + 105, + 662, + 505, + 674 + ], + "score": 1.0, + "content": "expresses preferences or intentions to harm others). This could therefore produce negative societal", + "type": "text" + } + ], + "index": 48 + }, + { + "bbox": [ + 105, + 672, + 486, + 685 + ], + "spans": [ + { + "bbox": [ + 105, + 672, + 486, + 685 + ], + "score": 1.0, + "content": "effects either if training leads to poor alignment or if agents are optimized for harmful metrics.", + "type": "text" + } + ], + "index": 49 + } + ], + "index": 46.5, + "bbox_fs": [ + 105, + 617, + 506, + 685 + ] + }, + { + "type": "text", + "bbox": [ + 108, + 689, + 504, + 722 + ], + "lines": [ + { + "bbox": [ + 106, + 689, + 505, + 701 + ], + "spans": [ + { + "bbox": [ + 106, + 689, + 505, + 701 + ], + "score": 1.0, + "content": "One potential strategy for mitigating these risks is the use of human preference data [15]. Such data", + "type": "text" + } + ], + "index": 50 + }, + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "spans": [ + { + "bbox": [ + 106, + 700, + 505, + 712 + ], + "score": 1.0, + "content": "could be used to fine-tune and filter trained agents before deployment, encouraging better alignment", + "type": "text" + } + ], + "index": 51 + }, + { + "bbox": [ + 106, + 711, + 505, + 723 + ], + "spans": [ + { + "bbox": [ + 106, + 711, + 505, + 723 + ], + "score": 1.0, + "content": "with human values. A key question in this line of research is how human preference data should be", + "type": "text" + } + ], + "index": 52 + }, + { + "bbox": [ + 105, + 73, + 505, + 85 + ], + "spans": [ + { + "bbox": [ + 105, + 73, + 505, + 85 + ], + "score": 1.0, + "content": "aggregated—or selected, in the case of expert preferences—when our aim is to create socially aligned", + "type": "text", + "cross_page": true + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 82, + 505, + 97 + ], + "spans": [ + { + "bbox": [ + 105, + 82, + 505, + 97 + ], + "score": 1.0, + "content": "agents (i.e. agents that are sufficiently aligned for everyone). Relatedly, targeted research on human", + "type": "text", + "cross_page": true + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 93, + 505, + 107 + ], + "spans": [ + { + "bbox": [ + 105, + 93, + 225, + 107 + ], + "score": 1.0, + "content": "beliefs and perceptions of AI", + "type": "text", + "cross_page": true + }, + { + "bbox": [ + 226, + 94, + 243, + 106 + ], + "score": 0.47, + "content": "\\pm 8 \\jmath", + "type": "inline_equation", + "cross_page": true + }, + { + "bbox": [ + 244, + 93, + 505, + 107 + ], + "score": 1.0, + "content": ", and how they steer human-agent interaction, would help inform", + "type": "text", + "cross_page": true + } + ], + "index": 2 + }, + { + "bbox": [ + 104, + 104, + 506, + 120 + ], + "spans": [ + { + "bbox": [ + 104, + 104, + 506, + 120 + ], + "score": 1.0, + "content": "agent design for positive societal impact. For instance, developers could incorporate specific priors", + "type": "text", + "cross_page": true + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 115, + 351, + 128 + ], + "spans": [ + { + "bbox": [ + 105, + 115, + 316, + 128 + ], + "score": 1.0, + "content": "into agents to reinforce tendencies for fair outcomes", + "type": "text", + "cross_page": true + }, + { + "bbox": [ + 317, + 116, + 349, + 128 + ], + "score": 0.28, + "content": "\\pm \\mathbb { Z } 0 . \\pm \\mathbb { B } 2 \\mathbb { I }", + "type": "inline_equation", + "cross_page": true + }, + { + "bbox": [ + 349, + 115, + 351, + 128 + ], + "score": 1.0, + "content": ".", + "type": "text", + "cross_page": true + } + ], + "index": 4 + } + ], + "index": 51, + "bbox_fs": [ + 106, + 689, + 505, + 723 + ] + } + ] + }, + { + "preproc_blocks": [ + { + "type": "text", + "bbox": [ + 107, + 73, + 505, + 127 + ], + "lines": [ + { + "bbox": [ + 105, + 73, + 505, + 85 + ], + "spans": [ + { + "bbox": [ + 105, + 73, + 505, + 85 + ], + "score": 1.0, + "content": "aggregated—or selected, in the case of expert preferences—when our aim is to create socially aligned", + "type": "text" + } + ], + "index": 0 + }, + { + "bbox": [ + 105, + 82, + 505, + 97 + ], + "spans": [ + { + "bbox": [ + 105, + 82, + 505, + 97 + ], + "score": 1.0, + "content": "agents (i.e. agents that are sufficiently aligned for everyone). Relatedly, targeted research on human", + "type": "text" + } + ], + "index": 1 + }, + { + "bbox": [ + 105, + 93, + 505, + 107 + ], + "spans": [ + { + "bbox": [ + 105, + 93, + 225, + 107 + ], + "score": 1.0, + "content": "beliefs and perceptions of AI", + "type": "text" + }, + { + "bbox": [ + 226, + 94, + 243, + 106 + ], + "score": 0.47, + "content": "\\pm 8 \\jmath", + "type": "inline_equation" + }, + { + "bbox": [ + 244, + 93, + 505, + 107 + ], + "score": 1.0, + "content": ", and how they steer human-agent interaction, would help inform", + "type": "text" + } + ], + "index": 2 + }, + { + "bbox": [ + 104, + 104, + 506, + 120 + ], + "spans": [ + { + "bbox": [ + 104, + 104, + 506, + 120 + ], + "score": 1.0, + "content": "agent design for positive societal impact. For instance, developers could incorporate specific priors", + "type": "text" + } + ], + "index": 3 + }, + { + "bbox": [ + 105, + 115, + 351, + 128 + ], + "spans": [ + { + "bbox": [ + 105, + 115, + 316, + 128 + ], + "score": 1.0, + "content": "into agents to reinforce tendencies for fair outcomes", + "type": "text" + }, + { + "bbox": [ + 317, + 116, + 349, + 128 + ], + "score": 0.28, + "content": "\\pm \\mathbb { Z } 0 . \\pm \\mathbb { B } 2 \\mathbb { I }", + "type": "inline_equation" + }, + { + "bbox": [ + 349, + 115, + 351, + 128 + ], + "score": 1.0, + "content": ".", + "type": "text" + } + ], + "index": 4 + } + ], + "index": 2 + }, + { + "type": "text", + "bbox": [ + 107, + 132, + 505, + 177 + ], + "lines": [ + { + "bbox": [ + 106, + 132, + 505, + 145 + ], + "spans": [ + { + "bbox": [ + 106, + 132, + 505, + 145 + ], + "score": 1.0, + "content": "Conclusion We proposed a method which is both effective at collaborating with humans and simple", + "type": "text" + } + ], + "index": 5 + }, + { + "bbox": [ + 105, + 144, + 505, + 156 + ], + "spans": [ + { + "bbox": [ + 105, + 144, + 505, + 156 + ], + "score": 1.0, + "content": "to implement. We also presented a rigorous and general methodology for evaluating with humans", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 155, + 505, + 168 + ], + "spans": [ + { + "bbox": [ + 105, + 155, + 505, + 168 + ], + "score": 1.0, + "content": "and eliciting their preferences. Together, these establish a strong foundation for future research on", + "type": "text" + } + ], + "index": 7 + }, + { + "bbox": [ + 105, + 164, + 413, + 179 + ], + "spans": [ + { + "bbox": [ + 105, + 164, + 413, + 179 + ], + "score": 1.0, + "content": "the important challenge of human-agent collaboration for benefiting society.", + "type": "text" + } + ], + "index": 8 + } + ], + "index": 6.5 + }, + { + "type": "title", + "bbox": [ + 108, + 192, + 207, + 205 + ], + "lines": [ + { + "bbox": [ + 105, + 190, + 208, + 208 + ], + "spans": [ + { + "bbox": [ + 105, + 190, + 208, + 208 + ], + "score": 1.0, + "content": "Acknowledgements", + "type": "text" + } + ], + "index": 9 + } + ], + "index": 9 + }, + { + "type": "text", + "bbox": [ + 107, + 216, + 505, + 260 + ], + "lines": [ + { + "bbox": [ + 105, + 215, + 505, + 228 + ], + "spans": [ + { + "bbox": [ + 105, + 215, + 505, + 228 + ], + "score": 1.0, + "content": "The authors would like to thank Mary Cassin for creating the game sprite art; Rohin Shah, Thore", + "type": "text" + } + ], + "index": 10 + }, + { + "bbox": [ + 106, + 226, + 505, + 238 + ], + "spans": [ + { + "bbox": [ + 106, + 226, + 505, + 238 + ], + "score": 1.0, + "content": "Graepel, and Iason Gabriel for feedback on the draft; Lucy Campbell-Gillingham, Tina Zhu, and", + "type": "text" + } + ], + "index": 11 + }, + { + "bbox": [ + 105, + 238, + 505, + 251 + ], + "spans": [ + { + "bbox": [ + 105, + 238, + 505, + 251 + ], + "score": 1.0, + "content": "Saffron Huang for support in evaluating agents with humans; and Max Kleiman-Weiner, Natasha", + "type": "text" + } + ], + "index": 12 + }, + { + "bbox": [ + 105, + 249, + 420, + 261 + ], + "spans": [ + { + "bbox": [ + 105, + 249, + 420, + 261 + ], + "score": 1.0, + "content": "Jaques, Marc Lanctot, Mike Bowling, and Dan Roberts for useful discussions.", + "type": "text" + } + ], + "index": 13 + } + ], + "index": 11.5 + }, + { + "type": "title", + "bbox": [ + 107, + 274, + 205, + 288 + ], + "lines": [ + { + "bbox": [ + 105, + 274, + 207, + 291 + ], + "spans": [ + { + "bbox": [ + 105, + 274, + 207, + 291 + ], + "score": 1.0, + "content": "Funding disclosure", + "type": "text" + } + ], + "index": 14 + } + ], + "index": 14 + }, + { + "type": "text", + "bbox": [ + 108, + 298, + 459, + 310 + ], + "lines": [ + { + "bbox": [ + 105, + 296, + 462, + 313 + ], + "spans": [ + { + "bbox": [ + 105, + 296, + 462, + 313 + ], + "score": 1.0, + "content": "This work was funded solely by DeepMind. 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We also presented a rigorous and general methodology for evaluating with humans", + "type": "text" + } + ], + "index": 6 + }, + { + "bbox": [ + 105, + 155, + 505, + 168 + ], + "spans": [ + { + "bbox": [ + 105, + 155, + 505, + 168 + ], + "score": 1.0, + "content": "and eliciting their preferences. 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