channel string | post_ids list | n_posts int64 | dt_utc_first timestamp[ns, tz=UTC] | dt_utc_last timestamp[ns, tz=UTC] | html_unwrapped string | label string | priority_max int64 |
|---|---|---|---|---|---|---|---|
AGI_and_RL | [
277
] | 1 | 2021-12-19T12:31:54Z | 2021-12-19T12:31:54Z | Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning <a href="https://arxiv.org/abs/2106.04895" rel="noopener" target="_blank">https://arxiv.org/abs/2106.04895</a> | tldr | 2 |
AGI_and_RL | [
279
] | 1 | 2022-01-25T11:07:24Z | 2022-01-25T11:07:24Z | <a href="https://distill.pub/2020/understanding-rl-vision/" rel="noopener" target="_blank">https://distill.pub/2020/understanding-rl-vision/</a> | tldr | 2 |
AGI_and_RL | [
280
] | 1 | 2022-02-02T17:04:59Z | 2022-02-02T17:04:59Z | DeepMind выпустил <a href="https://storage.googleapis.com/deepmind-media/AlphaCode/competition_level_code_generation_with_alphacode.pdf" onclick="return confirm('Open this link?\n\n'+this.href);" rel="noopener" target="_blank">AlphaCode</a>, который прогает лучше половины твоих знакомых.<br/><br/>Во многом похож на Cod... | tldr | 2 |
AGI_and_RL | [
281,
282
] | 2 | 2022-02-04T20:54:04Z | 2022-02-04T20:58:00Z | Formal Mathematics Statement Curriculum Learning <a href="https://arxiv.org/abs/2202.01344" rel="noopener" target="_blank">https://arxiv.org/abs/2202.01344</a>
<a href="https://github.com/openai/lean-gym" rel="noopener" target="_blank">https://github.com/openai/lean-gym</a><br/><a href="https://github.com/openai/miniF... | other | 2 |
AGI_and_RL | [
283
] | 1 | 2022-02-05T19:08:15Z | 2022-02-05T19:08:15Z | Modeling 3D Shapes by Reinforcement Learning <a href="https://arxiv.org/abs/2003.12397" rel="noopener" target="_blank">https://arxiv.org/abs/2003.12397</a> | tldr | 2 |
AGI_and_RL | [
284
] | 1 | 2022-02-08T17:11:28Z | 2022-02-08T17:11:28Z | <b>GPT + RL = Decision Making</b><br/><br/>Это гениально! Ребята из гугла показали, как с помощью RL можно научить <a href="https://d4mucfpksywv.cloudfront.net/better-language-models/language_models_are_unsupervised_multitask_learners.pdf" onclick="return confirm('Open this link?\n\n'+this.href);" rel="noopener" target... | tldr | 1 |
AGI_and_RL | [
285
] | 1 | 2022-02-09T09:59:02Z | 2022-02-09T09:59:02Z | <a href="https://ai.googleblog.com/2022/02/can-robots-follow-instructions-for-new.html" rel="noopener" target="_blank">https://ai.googleblog.com/2022/02/can-robots-follow-instructions-for-new.html</a> | tldr | 2 |
AGI_and_RL | [
286
] | 1 | 2022-02-11T22:16:04Z | 2022-02-11T22:16:04Z | <a href="https://deepmind.com/blog/article/MuZeros-first-step-from-research-into-the-real-world" rel="noopener" target="_blank">https://deepmind.com/blog/article/MuZeros-first-step-from-research-into-the-real-world</a> | tldr | 2 |
AGI_and_RL | [
287
] | 1 | 2022-02-18T14:22:08Z | 2022-02-18T14:22:08Z | На днях <a href="https://t.me/nn_for_science/836" onclick="return confirm('Open this link?\n\n'+this.href);" rel="noopener" target="_blank">писал про термояд</a> от DeepMind, а вот и блог-пост подъехал. Доступным и простым языком объясняют, что сделали и почему их исследование важно.<br/><br/><i class="emoji" style="ba... | other | 2 |
AGI_and_RL | [
288
] | 1 | 2022-02-19T14:17:51Z | 2022-02-19T14:17:51Z | <a href="https://www.youtube.com/watch?v=fEKZC9mta8w" rel="noopener" target="_blank">https://www.youtube.com/watch?v=fEKZC9mta8w</a> | other | 2 |
AGI_and_RL | [
289,
290
] | 2 | 2022-03-06T06:31:02Z | 2022-03-06T06:34:12Z | A Scalable Graph-Theoretic Distributed Framework for Cooperative Multi-Agent Reinforcement Learning <a href="https://arxiv.org/abs/2202.13046" rel="noopener" target="_blank">https://arxiv.org/abs/2202.13046</a>
Financial Vision Based Reinforcement Learning Trading Strategy <a href="https://arxiv.org/abs/2202.04115" re... | tldr | 2 |
AGI_and_RL | [
291
] | 1 | 2022-03-18T19:21:51Z | 2022-03-18T19:21:51Z | <a class="tgme_widget_message_author_name" href="https://t.me/AGI_and_RL"><span dir="auto">Агенты ИИ | AGI_and_RL</span></a> pinned «<span class="tgme_widget_service_strong_text" dir="auto">DeepMind выпустил AlphaCode, который прогает лучше половины твоих знакомых. Во многом похож на Codex, но есть отличия. Основной ... | other | 2 |
AGI_and_RL | [
292,
293
] | 2 | 2022-03-26T09:45:45Z | 2022-03-26T09:46:43Z | <b>A Survey of Multi-Agent Reinforcement Learning with Communication</b> <br/><a href="https://arxiv.org/abs/2203.08975" rel="noopener" target="_blank">https://arxiv.org/abs/2203.08975</a><br/>—-<br/>Communication is an effective mechanism for coordinating the behavior of multiple agents. In the field of multi-agent re... | tldr | 2 |
AGI_and_RL | [
294
] | 1 | 2022-04-13T22:43:04Z | 2022-04-13T22:43:04Z | <b>A Survey on Offline Reinforcement Learning: Taxonomy, Review, and Open Problems</b> <br/><a href="https://arxiv.org/abs/2203.01387" rel="noopener" target="_blank">https://arxiv.org/abs/2203.01387</a><br/>—-<br/>With the widespread adoption of deep learning, reinforcement learning (RL) has experienced a dramatic incr... | tldr | 2 |
AGI_and_RL | [
295,
296,
297,
298
] | 4 | 2022-04-17T11:05:42Z | 2022-04-17T11:19:40Z | <b>Reinforcement Learning on Graph: A Survey</b> <br/><a href="https://arxiv.org/abs/2204.06127" rel="noopener" target="_blank">https://arxiv.org/abs/2204.06127</a><br/>—-<br/>Graph mining tasks arise from many different application domains, ranging from social networks, transportation to E-commerce, etc., which have b... | tldr | 2 |
AGI_and_RL | [
300
] | 1 | 2022-04-25T18:51:43Z | 2022-04-25T18:51:43Z | <a href="https://habr.com/ru/company/dcmiran/blog/662866/" rel="noopener" target="_blank">https://habr.com/ru/company/dcmiran/blog/662866/</a> | other | 2 |
AGI_and_RL | [
301,
302
] | 2 | 2022-05-20T15:11:26Z | 2022-05-20T15:11:47Z | Online and Offline Reinforcement Learning by Planning with a Learned Model <a href="https://arxiv.org/abs/2104.06294" rel="noopener" target="_blank">https://arxiv.org/abs/2104.06294</a>
<a href="https://github.com/google-research/batch_rl" rel="noopener" target="_blank">https://github.com/google-research/batch_rl</a> | other | 2 |
AGI_and_RL | [
303,
304
] | 2 | 2022-05-22T21:27:54Z | 2022-05-22T21:41:27Z | [2105.08764] OpenGraphGym-MG: Using Reinforcement Learning to Solve Large Graph Optimization Problems on MultiGPU Systems<br/><a href="https://arxiv.org/abs/2105.08764" rel="noopener" target="_blank">https://arxiv.org/abs/2105.08764</a>
[2004.00530] Learning Sparse Rewarded Tasks from Sub-Optimal Demonstrations<br/><a... | tldr | 2 |
AGI_and_RL | [
305,
306,
307
] | 3 | 2022-06-06T09:01:08Z | 2022-06-06T09:02:02Z | Selective particle attention: Rapidly and flexibly selecting features for deep reinforcement learning <a href="https://www.sciencedirect.com/science/article/pii/S0893608022000934" rel="noopener" target="_blank">https://www.sciencedirect.com/science/article/pii/S0893608022000934</a>
TransDreamer: Reinforcement Learning... | tldr | 2 |
AGI_and_RL | [
308,
309,
310
] | 3 | 2022-06-08T04:46:29Z | 2022-06-08T04:47:21Z | Goal-Space Planning with Subgoal Models <a href="https://arxiv.org/abs/2206.02902" rel="noopener" target="_blank">https://arxiv.org/abs/2206.02902</a>
Policy Optimization for Markov Games: Unified Framework and Faster Convergence <a href="https://arxiv.org/abs/2206.02640" rel="noopener" target="_blank">https://arxiv.o... | tldr | 2 |
AGI_and_RL | [
311,
312
] | 2 | 2022-06-11T16:57:49Z | 2022-06-11T16:58:13Z | Recent Advances in Reinforcement Learning in Finance <a href="https://arxiv.org/abs/2112.04553" rel="noopener" target="_blank">https://arxiv.org/abs/2112.04553</a>
<a href="https://ai.googleblog.com/2021/04/evolving-reinforcement-learning.html" rel="noopener" target="_blank">https://ai.googleblog.com/2021/04/evolving-... | tldr | 2 |
AGI_and_RL | [
313,
314
] | 2 | 2022-06-11T17:40:28Z | 2022-06-11T17:41:37Z | AAM-Gym: Artificial Intelligence Testbed for Advanced Air Mobility <a href="https://arxiv.org/abs/2206.04513" rel="noopener" target="_blank">https://arxiv.org/abs/2206.04513</a>
Value Memory Graph: A Graph-Structured World Model for Offline Reinforcement Learning <a href="https://arxiv.org/abs/2206.04384" rel="noopene... | tldr | 2 |
AGI_and_RL | [
315,
316
] | 2 | 2022-06-15T23:16:14Z | 2022-06-15T23:16:50Z | <b>Reinforcement learning for combinatorial optimization: A survey <br/></b><a href="https://www.sciencedirect.com/science/article/abs/pii/S0305054821001660" rel="noopener" target="_blank">https://www.sciencedirect.com/science/article/abs/pii/S0305054821001660</a><br/>—-<br/>Many traditional algorithms for solving comb... | tldr | 2 |
AGI_and_RL | [
317
] | 1 | 2022-06-20T11:27:33Z | 2022-06-20T11:27:33Z | Variance Reduction for Policy-Gradient Methods via Empirical Variance Minimization <a href="https://arxiv.org/abs/2206.06827" rel="noopener" target="_blank">https://arxiv.org/abs/2206.06827</a> | tldr | 2 |
AGI_and_RL | [
318
] | 1 | 2022-07-09T21:03:01Z | 2022-07-09T21:03:01Z | PrefixRL: Optimization of Parallel Prefix Circuits using Deep Reinforcement Learning <a href="https://arxiv.org/abs/2205.07000" rel="noopener" target="_blank">https://arxiv.org/abs/2205.07000</a><br/><a href="https://developer.nvidia.com/blog/designing-arithmetic-circuits-with-deep-reinforcement-learning/" rel="noopene... | tldr | 2 |
AGI_and_RL | [
319
] | 1 | 2022-07-11T00:58:15Z | 2022-07-11T00:58:15Z | Stochastic optimal well control in subsurface reservoirs using reinforcement learning <a href="https://arxiv.org/abs/2207.03456" rel="noopener" target="_blank">https://arxiv.org/abs/2207.03456</a> | tldr | 2 |
AGI_and_RL | [
321
] | 1 | 2022-07-11T04:15:58Z | 2022-07-11T04:15:58Z | - AVDDPG: Federated reinforcement learning applied to autonomous platoon control. (arXiv:2207.03484v1 [cs.LG])<br/> <a href="http://arxiv.org/abs/2207.03484" rel="noopener" target="_blank">http://arxiv.org/abs/2207.03484</a> | tldr | 2 |
AGI_and_RL | [
322
] | 1 | 2022-07-13T06:14:07Z | 2022-07-13T06:14:07Z | - Fast and Optimal Adaptive Tracking Control: A Novel Meta-Reinforcement Learning via Conditional Generative Adversarial Net. (arXiv:2206.12450v1 [<a href="http://eess.SY/" rel="noopener" target="_blank">eess.SY</a>])<br/> <a href="http://arxiv.org/abs/2206.12450" rel="noopener" target="_blank">http://arxiv.org/abs/220... | tldr | 2 |
AGI_and_RL | [
323
] | 1 | 2022-07-17T16:33:17Z | 2022-07-17T16:33:17Z | Reinforcement Learning via AIXI Approximation <a href="https://ojs.aaai.org/index.php/AAAI/article/view/7667" rel="noopener" target="_blank">https://ojs.aaai.org/index.php/AAAI/article/view/7667</a> | tldr | 2 |
AGI_and_RL | [
324,
325
] | 2 | 2022-07-18T10:45:52Z | 2022-07-18T10:46:30Z | Learning Hierarchical Compositional Task Definitions through Online Situated Interactive Language Instruction <a href="https://deepblue.lib.umich.edu/handle/2027.42/153434" rel="noopener" target="_blank">https://deepblue.lib.umich.edu/handle/2027.42/153434</a>
<b>Universal Reinforcement Learning Algorithms: Survey and... | tldr | 2 |
AGI_and_RL | [
326
] | 1 | 2022-07-22T05:20:47Z | 2022-07-22T05:20:47Z | <a href="https://ai.googleblog.com/2022/07/training-generalist-agents-with-multi.html?m=1" rel="noopener" target="_blank">https://ai.googleblog.com/2022/07/training-generalist-agents-with-multi.html?m=1</a> | tldr | 2 |
AGI_and_RL | [
327,
328,
329
] | 3 | 2022-07-23T09:00:14Z | 2022-07-23T09:17:14Z | Hierarchical Reinforcement Learning for Air-to-Air Combat <a href="https://arxiv.org/abs/2105.00990" rel="noopener" target="_blank">https://arxiv.org/abs/2105.00990</a>
Reinforcement Learning Based Autonomous Air Combat with Energy Budgets <a href="https://arc.aiaa.org/doi/abs/10.2514/6.2022-0786" rel="noopener" targe... | tldr | 2 |
AGI_and_RL | [
330
] | 1 | 2022-07-27T06:43:40Z | 2022-07-27T06:43:40Z | <a href="https://diffusion-planning.github.io/" rel="noopener" target="_blank">https://diffusion-planning.github.io/</a> | other | 2 |
AGI_and_RL | [
331,
332
] | 2 | 2022-08-05T04:01:12Z | 2022-08-05T04:01:23Z | - Reinforcement Learning for Joint V2I Network Selection and Autonomous Driving Policies. (arXiv:2208.02249v1 [cs.LG])<br/> <a href="http://arxiv.org/abs/2208.02249" rel="noopener" target="_blank">http://arxiv.org/abs/2208.02249</a>
- Dynamic Planning in Open-Ended Dialogue using Reinforcement Learning. (arXiv:2208.02... | tldr | 2 |
AGI_and_RL | [
333
] | 1 | 2022-08-11T14:00:12Z | 2022-08-11T14:00:12Z | On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning <a href="https://arxiv.org/abs/2206.03271" rel="noopener" target="_blank">https://arxiv.org/abs/2206.03271</a> | other | 1 |
AGI_and_RL | [
334,
335,
336,
337,
338,
339
] | 6 | 2022-08-14T16:06:32Z | 2022-08-14T16:10:10Z | Model-Free Generative Replay for Lifelong Reinforcement Learning: Application to Starcraft-2 <a href="https://arxiv.org/abs/2208.05056" rel="noopener" target="_blank">https://arxiv.org/abs/2208.05056</a>
Reducing Exploitability with Population Based Training <a href="https://arxiv.org/abs/2208.05083" rel="noopener" ta... | other | 2 |
AGI_and_RL | [
340,
342,
343
] | 3 | 2022-08-16T16:56:19Z | 2022-08-16T17:02:06Z | Transformer-based Value Function Decomposition for Cooperative Multi-agent Reinforcement Learning in StarCraft <a href="https://arxiv.org/abs/2208.07298" rel="noopener" target="_blank">https://arxiv.org/abs/2208.07298</a>
RLang: A Declarative Language for Expression Prior Knowledge for Reinforcement Learning <a href="... | other | 2 |
AGI_and_RL | [
344,
345
] | 2 | 2022-08-17T15:02:24Z | 2022-08-17T15:02:42Z | Data-Efficient Reinforcement Learning with Self-Predictive Representations <a href="https://arxiv.org/abs/2007.05929" rel="noopener" target="_blank">https://arxiv.org/abs/2007.05929</a>
Zero-shot Text Classification via Reinforced Self-training <a href="https://aclanthology.org/2020.acl-main.272/" rel="noopener" targe... | tldr | 2 |
AGI_and_RL | [
346,
347
] | 2 | 2022-08-19T20:28:55Z | 2022-08-19T20:29:19Z | Designing, Modeling, and Optimizing Data-Intensive Computing Systems <a href="https://arxiv.org/abs/2208.08886" rel="noopener" target="_blank">https://arxiv.org/abs/2208.08886</a>
Data-driven End-to-end Learning of Pole Placement Control for Nonlinear Dynamics via Koopman Invariant Subspaces <a href="https://arxiv.org... | tldr | 2 |
AGI_and_RL | [
348
] | 1 | 2022-08-20T20:38:54Z | 2022-08-20T20:38:54Z | <a href="https://github.com/tensorforce/tensorforce" rel="noopener" target="_blank">https://github.com/tensorforce/tensorforce</a> | other | 2 |
AGI_and_RL | [
349,
350,
351,
352,
353,
354
] | 6 | 2022-08-23T09:24:01Z | 2022-08-23T09:53:18Z | BARReL: Bottleneck Attention for Adversarial Robustness in Vision-Based Reinforcement Learning <a href="https://arxiv.org/abs/2208.10481" rel="noopener" target="_blank">https://arxiv.org/abs/2208.10481</a>
Get It in Writing: Formal Contracts Mitigate Social Dilemmas in Multi-Agent RL <a href="https://arxiv.org/abs/220... | tldr | 2 |
AGI_and_RL | [
355,
356,
357
] | 3 | 2022-08-24T15:45:38Z | 2022-08-24T15:46:39Z | Computable Artificial General Intelligence <a href="https://arxiv.org/abs/2205.10513" rel="noopener" target="_blank">https://arxiv.org/abs/2205.10513</a>
Approaches to Artificial General Intelligence: An Analysis <a href="https://arxiv.org/abs/2202.03153" rel="noopener" target="_blank">https://arxiv.org/abs/2202.03153... | other | 2 |
AGI_and_RL | [
358,
359
] | 2 | 2022-08-24T16:49:17Z | 2022-08-24T16:49:22Z | Categorizing Wireheading in Partially Embedded Agents <a href="https://arxiv.org/abs/1906.09136" rel="noopener" target="_blank">https://arxiv.org/abs/1906.09136</a>
Computable Variants of AIXI which are More Powerful than AIXItl <a href="https://arxiv.org/abs/1805.08592" rel="noopener" target="_blank">https://arxiv.or... | tldr | 2 |
AGI_and_RL | [
360
] | 1 | 2022-08-27T13:53:33Z | 2022-08-27T13:53:33Z | GitHub - werner-duvaud/muzero-general: MuZero<br/><a href="https://github.com/werner-duvaud/muzero-general" rel="noopener" target="_blank">https://github.com/werner-duvaud/muzero-general</a> | tldr | 2 |
AGI_and_RL | [
361
] | 1 | 2022-08-27T20:52:36Z | 2022-08-27T20:52:36Z | <b>Mastering Atari Games with Limited Data</b> <a href="https://arxiv.org/abs/2111.00210" rel="noopener" target="_blank">https://arxiv.org/abs/2111.00210</a><br/>—-<br/>Reinforcement learning has achieved great success in many applications. However, sample efficiency remains a key challenge, with prominent methods requ... | tldr | 2 |
AGI_and_RL | [
363
] | 1 | 2022-08-28T00:05:33Z | 2022-08-28T00:05:33Z | Properties of Sparse Distributed Representations<br/>and their Application to Hierarchical Temporal<br/>Memory <a href="https://arxiv.org/abs/1503.07469" rel="noopener" target="_blank">https://arxiv.org/abs/1503.07469</a> | tldr | 2 |
AGI_and_RL | [
364
] | 1 | 2022-08-29T02:15:44Z | 2022-08-29T02:15:44Z | - Light-weight probing of unsupervised representations for Reinforcement Learning. (arXiv:2208.12345v1 [cs.LG])<br/> <a href="http://arxiv.org/abs/2208.12345" rel="noopener" target="_blank">http://arxiv.org/abs/2208.12345</a> | tldr | 2 |
AGI_and_RL | [
365
] | 1 | 2022-08-29T13:10:33Z | 2022-08-29T13:10:33Z | <a href="https://openai.com/blog/instruction-following/" rel="noopener" target="_blank">https://openai.com/blog/instruction-following/</a> | tldr | 2 |
AGI_and_RL | [
366
] | 1 | 2022-08-30T15:57:54Z | 2022-08-30T15:57:54Z | <b>A data-driven approach for learning to control computers</b> <a href="https://arxiv.org/abs/2202.08137" rel="noopener" target="_blank">https://arxiv.org/abs/2202.08137</a> | tldr | 2 |
AGI_and_RL | [
367,
368
] | 2 | 2022-08-31T16:51:52Z | 2022-08-31T16:52:47Z | <b>Proving Theorems using Incremental Learning and Hindsight Experience Replay<br/></b><a href="https://proceedings.mlr.press/v162/aygun22a.html" rel="noopener" target="_blank">https://proceedings.mlr.press/v162/aygun22a.html</a><br/>—-<br/>Traditional automated theorem proving systems for first-order logic depend on s... | tldr | 2 |
AGI_and_RL | [
369
] | 1 | 2022-08-31T19:13:16Z | 2022-08-31T19:13:16Z | HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving <a href="http://proceedings.mlr.press/v97/bansal19a.html" rel="noopener" target="_blank">http://proceedings.mlr.press/v97/bansal19a.html</a> | tldr | 2 |
AGI_and_RL | [
370
] | 1 | 2022-09-05T13:33:25Z | 2022-09-05T13:33:25Z | Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels? <a href="https://arxiv.org/abs/2206.05266" rel="noopener" target="_blank">https://arxiv.org/abs/2206.05266</a> | tldr | 2 |
AGI_and_RL | [
371,
372
] | 2 | 2022-09-06T12:35:12Z | 2022-09-06T12:36:14Z | Reinforcement Learning with Action-Free Pre-Training from Videos <a href="https://arxiv.org/abs/2203.13880" rel="noopener" target="_blank">https://arxiv.org/abs/2203.13880</a>
<b>Reinforcement Learning for Task Specifications with Action-Constraints</b> <a href="https://arxiv.org/abs/2201.00286" rel="noopener" target=... | tldr | 2 |
AGI_and_RL | [
373,
374
] | 2 | 2022-09-06T18:59:26Z | 2022-09-06T19:01:54Z | <b>Network Topology Optimization via Deep Reinforcement Learning</b> <a href="https://arxiv.org/abs/2204.14133" rel="noopener" target="_blank">https://arxiv.org/abs/2204.14133</a><br/>—-<br/>Topology impacts important network performance metrics, including link utilization, throughput and latency, and is of central imp... | tldr | 2 |
AGI_and_RL | [
375
] | 1 | 2022-09-06T19:39:35Z | 2022-09-06T19:39:35Z | <b>Self-Directed Online Machine Learning for Topology Optimization</b> <a href="https://arxiv.org/abs/2002.01927" rel="noopener" target="_blank">https://arxiv.org/abs/2002.01927</a><br/>—-<br/>Topology optimization by optimally distributing materials in a given domain requires non-gradient optimizers to solve highly co... | tldr | 2 |
AGI_and_RL | [
376
] | 1 | 2022-09-07T12:53:40Z | 2022-09-07T12:53:40Z | - Learning Practical Communication Strategies in Cooperative Multi-Agent Reinforcement Learning. (arXiv:2209.01288v1 [<a href="http://cs.AI/" rel="noopener" target="_blank">cs.AI</a>])<br/> <a href="http://arxiv.org/abs/2209.01288" rel="noopener" target="_blank">http://arxiv.org/abs/2209.01288</a> | tldr | 2 |
AGI_and_RL | [
377,
378,
379
] | 3 | 2022-09-09T19:00:33Z | 2022-09-09T19:20:55Z | <b>Interactive Imitation Learning in Robotics based on Simulations</b> <a href="https://arxiv.org/abs/2209.03900" rel="noopener" target="_blank">https://arxiv.org/abs/2209.03900</a><br/>—-<br/>The transformation towards intelligence in various industries is creating more demand for intelligent and flexible products. In... | tldr | 2 |
AGI_and_RL | [
380
] | 1 | 2022-09-10T15:45:43Z | 2022-09-10T15:45:43Z | <b>Multi-Task Reinforcement Learning with Context-based Representations</b> <br/><a href="https://arxiv.org/abs/2102.06177" rel="noopener" target="_blank">https://arxiv.org/abs/2102.06177</a><br/>—-<br/>The benefit of multi-task learning over single-task learning relies on the ability to use relations across tasks to i... | tldr | 2 |
AGI_and_RL | [
381,
382
] | 2 | 2022-09-12T07:53:08Z | 2022-09-12T07:54:29Z | <b>Multi-agent deep reinforcement learning: a survey </b><a href="https://link.springer.com/article/10.1007/s10462-021-09996-w" rel="noopener" target="_blank"><b>https://link.springer.com/article/10.1007/s10462-021-09996-w</b></a><b><br/></b>2022<br/>—-<br/>The advances in reinforcement learning have recorded sublime s... | other | 2 |
AGI_and_RL | [
384,
385
] | 2 | 2022-09-14T13:07:14Z | 2022-09-14T13:07:59Z | <b>Shaking the foundations: delusions in sequence models for interaction and control</b> <br/><a href="https://arxiv.org/abs/2110.10819" rel="noopener" target="_blank">https://arxiv.org/abs/2110.10819</a><br/>—-<br/>The recent phenomenal success of language models has reinvigorated machine learning research, and large ... | tldr | 2 |
AGI_and_RL | [
386,
387
] | 2 | 2022-09-20T02:33:57Z | 2022-09-20T02:36:35Z | <b>On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting <br/></b><a href="https://arxiv.org/abs/2206.00761" rel="noopener" target="_blank">https://arxiv.org/abs/2206.00761</a><br/>—-<br/>The availability of large pre-trained models is changing the landscape... | tldr | 2 |
AGI_and_RL | [
388,
389,
390
] | 3 | 2022-09-20T05:09:11Z | 2022-09-20T05:11:06Z | <b>A Learning-Based Trajectory Planning of Multiple UAVs for AoI Minimization in IoT Networks<br/></b><a href="https://arxiv.org/abs/2209.09206" rel="noopener" target="_blank">https://arxiv.org/abs/2209.09206</a><br/>—-<br/>Many emerging Internet of Things (IoT) applications rely on information collected by sensor node... | tldr | 2 |
AGI_and_RL | [
392
] | 1 | 2022-09-24T17:10:25Z | 2022-09-24T17:10:25Z | <b>A review of AI-enabled routing protocols for UAV networks: Trends, challenges, and future outlook<br/></b><a href="https://www.sciencedirect.com/science/article/pii/S1570870522000087" rel="noopener" target="_blank">https://www.sciencedirect.com/science/article/pii/S1570870522000087</a><br/>—-<br/>Unmanned Aerial Veh... | tldr | 2 |
AGI_and_RL | [
393
] | 1 | 2022-09-30T14:25:23Z | 2022-09-30T14:25:23Z | Нашёл максимально компактный обзор всех RL-подходов (<a href="https://arxiv.org/pdf/2209.14940.pdf" onclick="return confirm('Open this link?\n\n'+this.href);" rel="noopener" target="_blank">тык</a>) | tldr | 2 |
AGI_and_RL | [
394
] | 1 | 2022-10-05T15:54:08Z | 2022-10-05T15:54:08Z | <a href="https://www.nature.com/articles/s41586-022-05172-4" rel="noopener" target="_blank">https://www.nature.com/articles/s41586-022-05172-4</a><br/><a href="https://github.com/deepmind/alphatensor" rel="noopener" target="_blank">https://github.com/deepmind/alphatensor</a> | tldr | 2 |
AGI_and_RL | [
395,
396,
397
] | 3 | 2022-10-06T18:58:42Z | 2022-10-06T19:05:37Z | <b>Диффузия оптимизирует нейронки</b><br/><br/>А вот <a href="https://t.me/dlinnlp/1411" onclick="return confirm('Open this link?\n\n'+this.href);" rel="noopener" target="_blank">тут</a> вышла статья, где с помощью диффузионной модели заменяют традиционные оптимизаторы типа SGD или ADAM. <br/><br/>На вход поступают тек... | tldr | 2 |
AGI_and_RL | [
398,
399,
400
] | 3 | 2022-10-09T04:17:51Z | 2022-10-09T04:21:24Z | <b>Using Deep Reinforcement Learning for Zero Defect Smart Forging<br/></b><a href="https://arxiv.org/abs/2201.10268" rel="noopener" target="_blank">https://arxiv.org/abs/2201.10268</a><br/>—-<br/>Defects during production may lead to material waste, which is a significant challenge for many companies as it reduces rev... | tldr | 2 |
AGI_and_RL | [
401
] | 1 | 2022-10-23T23:49:24Z | 2022-10-23T23:49:24Z | <b>Learning to Synthesize Programs as Interpretable and Generalizable Policies</b> <br/><a href="https://arxiv.org/abs/2108.13643" rel="noopener" target="_blank">https://arxiv.org/abs/2108.13643</a><br/>—-<br/>Recently, deep reinforcement learning (DRL) methods have achieved impressive performance on tasks in a variety... | tldr | 2 |
AGI_and_RL | [
402
] | 1 | 2022-10-24T14:33:38Z | 2022-10-24T14:33:38Z | <b>Lessons from AlphaZero for Optimal, Model Predictive, and Adaptive Control<br/></b><a href="https://arxiv.org/abs/2108.10315" rel="noopener" target="_blank">https://arxiv.org/abs/2108.10315</a><br/>—-<br/>In this paper we aim to provide analysis and insights (often based on visualization), which explain the benefici... | tldr | 2 |
AGI_and_RL | [
403
] | 1 | 2022-10-25T15:05:04Z | 2022-10-25T15:05:04Z | "Large language models can write informal proofs, translate them into formal ones, and achieve SoTA performance in proving competition-level maths problems!"<br/><br/><a href="https://arxiv.org/abs/2210.12283" rel="noopener" target="_blank">https://arxiv.org/abs/2210.12283</a> <br/><br/>This is one of many recent resul... | tldr | 2 |
AGI_and_RL | [
404,
406,
407,
408
] | 4 | 2022-10-25T19:35:16Z | 2022-10-25T20:00:13Z | <b>Exploring search space trees using an adapted version of Monte Carlo tree search for combinatorial optimization problems<br/></b><a href="https://arxiv.org/abs/2010.11523" rel="noopener" target="_blank">https://arxiv.org/abs/2010.11523</a><br/>—-<br/>In this article, a novel approach to solve combinatorial optimizat... | tldr | 2 |
AGI_and_RL | [
410
] | 1 | 2022-11-27T21:57:50Z | 2022-11-27T21:57:50Z | The 37 Implementation Details of Proximal Policy Optimization<br/><a href="https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/" rel="noopener" target="_blank">https://iclr-blog-track.github.io/2022/03/25/ppo-implementation-details/</a> | tldr | 2 |
AGI_and_RL | [
411
] | 1 | 2022-11-28T19:21:48Z | 2022-11-28T19:21:48Z | <b>MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning<br/></b><a href="https://arxiv.org/abs/2211.13382" rel="noopener" target="_blank">https://arxiv.org/abs/2211.13382</a><br/><b>24 Nov 2022<br/></b>—-<br/>Placement is an essential task in modern chip design, aiming at placing millions of cir... | tldr | 2 |
AGI_and_RL | [
412,
413,
414
] | 3 | 2022-12-12T12:27:31Z | 2022-12-12T12:33:19Z | <b>Multi-agent Reinforcement Learning for Networked System Control<br/></b><a href="https://arxiv.org/abs/2004.01339" rel="noopener" target="_blank">https://arxiv.org/abs/2004.01339</a><br/><b>24 Apr 2020<br/></b>—-<br/>This paper considers multi-agent reinforcement learning (MARL) in networked system control. Specific... | tldr | 2 |
AGI_and_RL | [
415,
416,
417,
418,
419
] | 5 | 2023-01-08T09:41:29Z | 2023-01-08T10:10:36Z | <b>Self-Motivated Multi-Agent Exploration<br/></b><a href="https://arxiv.org/abs/2301.02083" rel="noopener" target="_blank">https://arxiv.org/abs/2301.02083</a><br/><b>5 Jan 2023<br/></b>—-<br/>In cooperative multi-agent reinforcement learning (CMARL), it is critical for agents to achieve a balance between self-explora... | tldr | 2 |
AGI_and_RL | [
420,
421
] | 2 | 2023-01-15T03:41:32Z | 2023-01-15T03:42:45Z | <b>Predictive World Models from Real-World Partial Observations<br/></b><a href="https://arxiv.org/abs/2301.04783" rel="noopener" target="_blank">https://arxiv.org/abs/2301.04783</a><br/><b>12 Jan 2023<br/></b>—-<br/>Cognitive scientists believe adaptable intelligent agents like humans perform reasoning through learned... | tldr | 2 |
AGI_and_RL | [
422,
423
] | 2 | 2023-01-16T13:23:00Z | 2023-01-16T13:26:55Z | Статейка с разбором подходов adversarial атаки и защиты на RL алгоритмы 2019 года:<br/><b>Adversarial attack and defense in reinforcement learning-from AI security view</b> <br/><a href="https://cybersecurity.springeropen.com/articles/10.1186/s42400-019-0027-x" rel="noopener" target="_blank">https://cybersecurity.sprin... | tldr | 2 |
AGI_and_RL | [
424
] | 1 | 2023-01-19T15:51:00Z | 2023-01-19T15:51:00Z | <a href="https://sites.google.com/view/adaptive-agent/" rel="noopener" target="_blank">https://sites.google.com/view/adaptive-agent/</a><br/><br/><b>Human-Timescale Adaptation in an Open-Ended Task Space<br/></b><a href="https://arxiv.org/abs/2301.07608" rel="noopener" target="_blank">https://arxiv.org/abs/2301.07608</... | tldr | 2 |
AGI_and_RL | [
425
] | 1 | 2023-01-26T06:55:27Z | 2023-01-26T06:55:27Z | <i class="emoji" style="background-image:url('//telegram.org/img/emoji/40/F09FA497.png')"><b>🤗</b></i><b>Illustrated Reinforcement Learning from Human Feedback (RLHF)</b><br/><br/>Отличный блог-пост от HuggingFace с разбором RL для файнтюна языковых моделей (<a href="https://t.me/abstractDL/118" onclick="return confir... | other | 2 |
AGI_and_RL | [
426,
428,
429,
430,
431,
432,
433
] | 7 | 2023-01-27T11:26:44Z | 2023-01-27T11:39:42Z | <b>Which Experiences Are Influential for Your Agent? Policy Iteration with Turn-over Dropout<br/></b><a href="https://arxiv.org/abs/2301.11168" rel="noopener" target="_blank">https://arxiv.org/abs/2301.11168</a><br/><b>26 Jan 2023<br/></b>—-<br/>In reinforcement learning (RL) with experience replay, experiences stored ... | tldr | 2 |
AGI_and_RL | [
434
] | 1 | 2023-01-29T19:26:12Z | 2023-01-29T19:26:12Z | <b>A Survey on Explainable Reinforcement Learning: Concepts, Algorithms, Challenges<br/></b><a href="https://paperswithcode.com/paper/a-survey-on-explainable-reinforcement" rel="noopener" target="_blank">https://paperswithcode.com/paper/a-survey-on-explainable-reinforcement</a><br/><b>12 Nov 2022<br/></b>—-<br/>Reinfor... | tldr | 2 |
AGI_and_RL | [
435,
436
] | 2 | 2023-01-30T18:37:29Z | 2023-01-30T19:07:28Z | <b>SNeRL: Semantic-aware Neural Radiance Fields for Reinforcement Learning<br/></b><a href="https://arxiv.org/abs/2301.11520" rel="noopener" target="_blank">https://arxiv.org/abs/2301.11520</a><br/><b>27 Jan 2023<br/></b>—-<br/>As previous representations for reinforcement learning cannot effectively incorporate a huma... | tldr | 2 |
AGI_and_RL | [
437
] | 1 | 2023-01-31T09:04:00Z | 2023-01-31T09:04:00Z | <b>Hierarchical Programmatic Reinforcement Learning via Learning to Compose Programs<br/></b><a href="https://arxiv.org/abs/2301.12950" rel="noopener" target="_blank">https://arxiv.org/abs/2301.12950</a><br/><b>30 Jan 2023<br/></b>—-<br/>Aiming to produce reinforcement learning (RL) policies that are human-interpretabl... | tldr | 1 |
AGI_and_RL | [
438
] | 1 | 2023-01-31T11:23:48Z | 2023-01-31T11:23:48Z | Sample Efficient Deep Reinforcement Learning via Local Planning <a href="https://arxiv.org/abs/2301.12579" rel="noopener" target="_blank">https://arxiv.org/abs/2301.12579</a> | tldr | 2 |
AGI_and_RL | [
439,
440,
441,
442,
443
] | 5 | 2023-02-02T12:37:41Z | 2023-02-02T12:40:22Z | <b>Learning Cut Selection for Mixed-Integer Linear Programming via Hierarchical Sequence Model<br/></b><a href="https://arxiv.org/abs/2302.00244" rel="noopener" target="_blank">https://arxiv.org/abs/2302.00244</a><br/><b>1 Feb 2023<br/></b>—-<br/>Cutting planes (cuts) are important for solving mixed-integer linear prog... | tldr | 2 |
AGI_and_RL | [
444
] | 1 | 2023-02-03T04:38:02Z | 2023-02-03T04:38:02Z | <b>Reinforcement learning for electric vehicle applications in power systems:A critical review<br/></b><a href="https://www.sciencedirect.com/science/article/pii/S1364032122009339" rel="noopener" target="_blank">https://www.sciencedirect.com/science/article/pii/S1364032122009339</a><br/><b>29 Nov 2022<br/></b>Electric ... | tldr | 2 |
AGI_and_RL | [
445
] | 1 | 2023-02-05T23:08:42Z | 2023-02-05T23:08:42Z | <b>What Matters In On-Policy Reinforcement Learning? A Large-Scale Empirical Study<br/></b><a href="https://arxiv.org/abs/2006.05990" rel="noopener" target="_blank">https://arxiv.org/abs/2006.05990</a><br/><b>10 Jun 2020<br/></b>—-<br/>In recent years, on-policy reinforcement learning (RL) has been successfully applied... | tldr | 2 |
AGI_and_RL | [
446,
447
] | 2 | 2023-02-07T21:01:25Z | 2023-02-07T21:02:15Z | <b>Distributional constrained reinforcement learning for supply chain optimization<br/></b><a href="https://paperswithcode.com/paper/distributional-constrained-reinforcement" rel="noopener" target="_blank">https://paperswithcode.com/paper/distributional-constrained-reinforcement</a><br/><b>3 Feb 2023<br/></b>—-<br/>Thi... | tldr | 2 |
AGI_and_RL | [
449,
450,
451,
452,
453,
454
] | 6 | 2023-02-10T16:56:55Z | 2023-02-10T17:30:27Z | <b>What are the mechanisms underlying metacognitive learning?<br/></b><a href="https://arxiv.org/abs/2302.04840" rel="noopener" target="_blank">https://arxiv.org/abs/2302.04840</a><br/><b>9 Feb 2023<br/></b>—-<br/>How is it that humans can solve complex planning tasks so efficiently despite limited cognitive resources?... | tldr | 2 |
AGI_and_RL | [
456,
457,
458,
459
] | 4 | 2023-02-13T06:21:35Z | 2023-02-13T06:26:31Z | <b>A Survey on Causal Reinforcement Learning<br/></b><a href="https://arxiv.org/abs/2302.05209" rel="noopener" target="_blank">https://arxiv.org/abs/2302.05209</a><br/><b>10 Feb 2023<br/></b>—-<br/>While Reinforcement Learning (RL) achieves tremendous success in sequential decision-making problems of many domains, it s... | tldr | 2 |
AGI_and_RL | [
460,
461
] | 2 | 2023-02-13T08:57:06Z | 2023-02-13T08:58:28Z | <b>Best Possible Q-Learning<br/></b><a href="https://arxiv.org/abs/2302.01188" rel="noopener" target="_blank">https://arxiv.org/abs/2302.01188</a><br/><b>2 Feb 2023<br/></b>—-<br/>Fully decentralized learning, where the global information, i.e., the actions of other agents, is inaccessible, is a fundamental challenge i... | tldr | 1 |
AGI_and_RL | [
462,
463,
464,
465,
466,
467,
468
] | 7 | 2023-02-14T12:06:04Z | 2023-02-14T12:10:14Z | <b>Robust Knowledge Transfer in Tiered Reinforcement Learning<br/></b><a href="https://arxiv.org/abs/2302.05534" rel="noopener" target="_blank">https://arxiv.org/abs/2302.05534</a><br/><b>10 Feb 2023<br/></b>—-<br/>In this paper, we study the Tiered Reinforcement Learning setting, a parallel transfer learning framework... | tldr | 2 |
AGI_and_RL | [
469
] | 1 | 2023-02-14T15:40:22Z | 2023-02-14T15:40:22Z | <b>Sample-Efficient Model-Free Reinforcement Learning with Off-Policy Critics<br/></b><a href="https://arxiv.org/abs/1903.04193" rel="noopener" target="_blank">https://arxiv.org/abs/1903.04193</a><br/><b>12 Jun 2019<br/></b>—-<br/>Value-based reinforcement-learning algorithms provide state-of-the-art results in model-f... | tldr | 2 |
AGI_and_RL | [
471
] | 1 | 2023-02-15T12:01:07Z | 2023-02-15T12:01:07Z | <b>Semiconductor Fab Scheduling with Self-Supervised and Reinforcement Learning<br/></b><a href="https://arxiv.org/abs/2302.07162" rel="noopener" target="_blank">https://arxiv.org/abs/2302.07162</a><br/><b>14 Feb 2023<br/></b>—-<br/>Semiconductor manufacturing is a notoriously complex and costly multi-step process invo... | tldr | 2 |
AGI_and_RL | [
472,
473,
475,
476,
477,
478
] | 6 | 2023-02-16T08:10:57Z | 2023-02-16T08:36:48Z | <b>Informational active matter<br/></b><a href="https://arxiv.org/abs/2302.07402" rel="noopener" target="_blank">https://arxiv.org/abs/2302.07402</a><br/><b>14 Feb 2023<br/></b>—-<br/>Many biomolecular systems can be viewed as ratchets that rectify environmental noise through measurements and information processing. As... | tldr | 2 |
AGI_and_RL | [
479,
480,
481,
482,
483,
484,
485,
486,
487
] | 9 | 2023-02-17T13:10:01Z | 2023-02-17T13:21:32Z | <b>Learning Random Access Schemes for Massive Machine-Type Communication with MARL<br/></b><a href="https://arxiv.org/abs/2302.07837" rel="noopener" target="_blank">https://arxiv.org/abs/2302.07837</a><br/><b>15 Feb 2023<br/></b>—-<br/>In this paper, we explore various multi-agent reinforcement learning (MARL) techniqu... | tldr | 2 |
AGI_and_RL | [
488,
489
] | 2 | 2023-02-19T12:31:57Z | 2023-02-19T12:36:49Z | UCL Course on RL<br/><a href="https://www.davidsilver.uk/teaching/" rel="noopener" target="_blank">https://www.davidsilver.uk/teaching/</a>
Causal Reinforcement Learning <a href="https://crl.causalai.net/" rel="noopener" target="_blank">https://crl.causalai.net/</a> | other | 2 |
AGI_and_RL | [
490,
491,
493,
494
] | 4 | 2023-02-22T16:10:16Z | 2023-02-22T16:21:39Z | <b>Neural Optimal Control using Learned System Dynamics<br/></b><a href="https://arxiv.org/abs/2302.09846" rel="noopener" target="_blank">https://arxiv.org/abs/2302.09846</a><br/><b>20 Feb 2023<br/></b>—-<br/>We study the problem of generating control laws for systems with unknown dynamics. Our approach is to represent... | tldr | 2 |
AGI_and_RL | [
495,
496
] | 2 | 2023-02-23T18:20:35Z | 2023-02-23T18:26:04Z | <b>Behavior Proximal Policy Optimization<br/></b><a href="https://arxiv.org/abs/2302.11312" rel="noopener" target="_blank">https://arxiv.org/abs/2302.11312</a><br/><b>22 Feb 2023<br/></b>—-<br/>Offline reinforcement learning (RL) is a challenging setting where existing off-policy actor-critic methods perform poorly due... | tldr | 2 |
AGI_and_RL | [
497
] | 1 | 2023-02-24T23:03:35Z | 2023-02-24T23:03:35Z | <i class="emoji" style="background-image:url('//telegram.org/img/emoji/40/F09F94A5.png')"><b>🔥</b></i><b>FlexGen:</b> <b>как запустить OPT-175B на своём ноутбуке</b><br/><br/>Просто восторг! <a href="https://t.me/abstractDL/143" onclick="return confirm('Open this link?\n\n'+this.href);" rel="noopener" target="_blank">... | tldr | 2 |
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