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 | [
141
] | 1 | 2020-11-16T14:48:42Z | 2020-11-16T14:48:42Z | <b>Active Reinforcement Learning: Observing Rewards at a Cost</b> <a href="https://arxiv.org/abs/2011.06709" rel="noopener" target="_blank">https://arxiv.org/abs/2011.06709</a> | other | 1 |
AGI_and_RL | [
142
] | 1 | 2020-11-16T16:26:35Z | 2020-11-16T16:26:35Z | <a href="https://twitter.com/DeepMind/status/1328370419158708224?s=19" rel="noopener" target="_blank">https://twitter.com/DeepMind/status/1328370419158708224?s=19</a> | other | 1 |
AGI_and_RL | [
143
] | 1 | 2020-11-17T00:11:45Z | 2020-11-17T00:11:45Z | Transfer among Agents: An Efficient Multiagent Transfer Learning Framework <a href="https://arxiv.org/abs/2002.08030" rel="noopener" target="_blank">https://arxiv.org/abs/2002.08030</a> | other | 1 |
AGI_and_RL | [
145
] | 1 | 2020-11-17T11:02:22Z | 2020-11-17T11:02:22Z | <b>Robust Quadruped Jumping via Deep Reinforcement Learning</b> <a href="https://arxiv.org/abs/2011.07089" rel="noopener" target="_blank">https://arxiv.org/abs/2011.07089</a> | other | 1 |
AGI_and_RL | [
146
] | 1 | 2020-11-17T17:44:37Z | 2020-11-17T17:44:37Z | <b>SoftGym: Benchmarking Deep Reinforcement Learning for Deformable Object Manipulation</b> <a href="https://arxiv.org/abs/2011.07215" rel="noopener" target="_blank">https://arxiv.org/abs/2011.07215</a> | other | 1 |
AGI_and_RL | [
147
] | 1 | 2020-11-17T22:09:47Z | 2020-11-17T22:09:47Z | Domain-Invariant Representation Learning for Sim-to-Real Transfer <a href="https://arxiv.org/abs/2011.07589" rel="noopener" target="_blank">https://arxiv.org/abs/2011.07589</a> | other | 1 |
AGI_and_RL | [
149
] | 1 | 2020-11-18T18:28:27Z | 2020-11-18T18:28:27Z | <b>Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization</b> <a href="https://arxiv.org/abs/2011.08541" rel="noopener" target="_blank">https://arxiv.org/abs/2011.08541</a> | other | 1 |
AGI_and_RL | [
150
] | 1 | 2020-11-18T20:57:47Z | 2020-11-18T20:57:47Z | Distilling a Hierarchical Policy for Planning and Control via Representation and Reinforcement Learning <a href="https://arxiv.org/abs/2011.08345" rel="noopener" target="_blank">https://arxiv.org/abs/2011.08345</a> | other | 1 |
AGI_and_RL | [
151,
152
] | 2 | 2020-11-20T06:44:07Z | 2020-11-20T06:44:17Z | Multi-agent Hierarchical Reinforcement Learning with Dynamic Termination <a href="https://arxiv.org/abs/1910.09508" rel="noopener" target="_blank">https://arxiv.org/abs/1910.09508</a>
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery <a href="https://arxiv.org/abs/1912.03558" rel="noopen... | other | 1 |
AGI_and_RL | [
153
] | 1 | 2020-11-21T01:18:15Z | 2020-11-21T01:18:15Z | <a href="https://github.com/sweetice/Deep-reinforcement-learning-with-pytorch" rel="noopener" target="_blank">https://github.com/sweetice/Deep-reinforcement-learning-with-pytorch</a> | other | 1 |
AGI_and_RL | [
154
] | 1 | 2020-11-21T06:58:12Z | 2020-11-21T06:58:12Z | <a href="https://towardsdatascience.com/openai-gym-from-scratch-619e39af121f" rel="noopener" target="_blank">https://towardsdatascience.com/openai-gym-from-scratch-619e39af121f</a> | other | 1 |
AGI_and_RL | [
155,
156
] | 2 | 2020-11-22T06:27:41Z | 2020-11-22T06:35:55Z | Avoiding Tampering Incentives in Deep RL via Decoupled Approval <a href="https://arxiv.org/abs/2011.08827" rel="noopener" target="_blank">https://arxiv.org/abs/2011.08827</a>
<a href="https://deepai.org/publication/tonic-a-deep-reinforcement-learning-library-for-fast-prototyping-and-benchmarking" rel="noopener" target... | other | 1 |
AGI_and_RL | [
157,
158,
159
] | 3 | 2020-11-24T10:25:01Z | 2020-11-24T10:25:36Z | <a href="https://github.com/oxwhirl/wqmix" rel="noopener" target="_blank">https://github.com/oxwhirl/wqmix</a>
<a href="https://github.com/openai/maddpg" rel="noopener" target="_blank">https://github.com/openai/maddpg</a>
<a href="https://github.com/xuehy/pytorch-maddpg" rel="noopener" target="_blank">https://github.... | other | 1 |
AGI_and_RL | [
160
] | 1 | 2020-11-24T15:07:01Z | 2020-11-24T15:07:01Z | Actor-Attention-Critic for Multi-Agent Reinforcement Learning <a href="https://arxiv.org/abs/1810.02912" rel="noopener" target="_blank">https://arxiv.org/abs/1810.02912</a> | other | 1 |
AGI_and_RL | [
161
] | 1 | 2020-11-24T16:32:20Z | 2020-11-24T16:32:20Z | Learning Attentional Communication for Multi-Agent Cooperation <a href="https://papers.nips.cc/paper/2018/file/6a8018b3a00b69c008601b8becae392b-Paper.pdf" rel="noopener" target="_blank">https://papers.nips.cc/paper/2018/file/6a8018b3a00b69c008601b8becae392b-Paper.pdf</a> | other | 1 |
AGI_and_RL | [
162,
163,
164
] | 3 | 2020-11-27T15:17:40Z | 2020-11-27T15:22:46Z | <a href="https://jonathan-hui.medium.com/rl-dqn-deep-q-network-e207751f7ae4" rel="noopener" target="_blank">https://jonathan-hui.medium.com/rl-dqn-deep-q-network-e207751f7ae4</a>
<a href="https://github.com/deepmind/dqn" rel="noopener" target="_blank">https://github.com/deepmind/dqn</a>
Train a software agent to beha... | other | 1 |
AGI_and_RL | [
165
] | 1 | 2020-11-27T15:55:14Z | 2020-11-27T15:55:14Z | A Deep Reinforcement Learning Approach to First-Order Logic Theorem Proving <a href="https://arxiv.org/pdf/1911.02065.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/1911.02065.pdf</a> | other | 1 |
AGI_and_RL | [
167,
168
] | 2 | 2020-11-29T14:57:13Z | 2020-11-29T15:02:24Z | <a href="https://sim2real.github.io/#papers" rel="noopener" target="_blank">https://sim2real.github.io/#papers</a>
<a href="https://www.youtube.com/watch?v=LeAjXQe8Sik" rel="noopener" target="_blank">https://www.youtube.com/watch?v=LeAjXQe8Sik</a> | other | 1 |
AGI_and_RL | [
169
] | 1 | 2020-12-02T11:46:50Z | 2020-12-02T11:46:50Z | Modelling the Dynamic Joint Policy of Teammates with Attention Multi-agent DDPG <a href="https://arxiv.org/abs/1811.07029" rel="noopener" target="_blank">https://arxiv.org/abs/1811.07029</a> | other | 1 |
AGI_and_RL | [
170
] | 1 | 2020-12-04T09:40:01Z | 2020-12-04T09:40:01Z | Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER <a href="https://deepai.org/publication/convergence-proof-for-actor-critic-methods-applied-to-ppo-and-rudder" rel="noopener" target="_blank">https://deepai.org/publication/convergence-proof-for-actor-critic-methods-applied-to-ppo-and-rudder</a> | other | 1 |
AGI_and_RL | [
171
] | 1 | 2020-12-05T08:40:29Z | 2020-12-05T08:40:29Z | <a href="http://www.sr-sv.com/real-time-growth-estimation-with-reinforcement-learning/" rel="noopener" target="_blank">http://www.sr-sv.com/real-time-growth-estimation-with-reinforcement-learning/</a> | other | 1 |
AGI_and_RL | [
172
] | 1 | 2020-12-05T15:14:28Z | 2020-12-05T15:14:28Z | Quantum Material Synthesis by Reinforcement Learning <a href="https://ml4physicalsciences.github.io/2020/files/NeurIPS_ML4PS_2020_123.pdf" rel="noopener" target="_blank">https://ml4physicalsciences.github.io/2020/files/NeurIPS_ML4PS_2020_123.pdf</a> | other | 1 |
AGI_and_RL | [
173
] | 1 | 2020-12-06T09:28:37Z | 2020-12-06T09:28:37Z | <b>Adaptive Partial Scanning Transmission Electron Microscopy with Reinforcement Learning</b> <a href="https://arxiv.org/abs/2004.02786" rel="noopener" target="_blank">https://arxiv.org/abs/2004.02786</a> | other | 1 |
AGI_and_RL | [
174
] | 1 | 2020-12-07T19:01:47Z | 2020-12-07T19:01:47Z | Cluster Based Deep Contextual Reinforcement Learning for top-k Recommendations. <a href="https://arxiv.org/abs/2012.02291" rel="noopener" target="_blank">https://arxiv.org/abs/2012.02291</a> | tldr | 2 |
AGI_and_RL | [
175
] | 1 | 2020-12-08T04:48:23Z | 2020-12-08T04:48:23Z | <b>Data Boost: Text Data Augmentation Through Reinforcement Learning Guided Conditional Generation</b><br/><a href="https://arxiv.org/abs/2012.02952" rel="noopener" target="_blank">https://arxiv.org/abs/2012.02952</a> | tldr | 2 |
AGI_and_RL | [
176
] | 1 | 2020-12-09T05:35:51Z | 2020-12-09T05:35:51Z | <a href="https://deepai.org/publication/efficient-reservoir-management-through-deep-reinforcement-learning" rel="noopener" target="_blank">https://deepai.org/publication/efficient-reservoir-management-through-deep-reinforcement-learning</a> | tldr | 2 |
AGI_and_RL | [
177
] | 1 | 2020-12-09T17:14:12Z | 2020-12-09T17:14:12Z | <a href="https://deepai.org/publication/gpu-accelerated-exhaustive-search-for-optimal-ensemble-of-black-box-optimization-algorithms" rel="noopener" target="_blank">https://deepai.org/publication/gpu-accelerated-exhaustive-search-for-optimal-ensemble-of-black-box-optimization-algorithms</a> | tldr | 2 |
AGI_and_RL | [
178
] | 1 | 2020-12-10T03:26:14Z | 2020-12-10T03:26:14Z | TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search <a href="https://papers.nips.cc/paper/2020/hash/e904831f48e729f9ad8355a894334700-Abstract.html" rel="noopener" target="_blank">https://papers.nips.cc/paper/2020/hash/e904831f48e729f9ad8355a894334700-Abstract.html</a> | tldr | 2 |
AGI_and_RL | [
179
] | 1 | 2020-12-13T10:40:34Z | 2020-12-13T10:40:34Z | JAX MD: A Framework for Differentiable Physics <a href="https://github.com/google/jax-md" rel="noopener" target="_blank">https://github.com/google/jax-md</a> | tldr | 2 |
AGI_and_RL | [
180
] | 1 | 2020-12-15T21:09:18Z | 2020-12-15T21:09:18Z | <a href="https://deepai.org/publication/semi-supervised-reward-learning-for-offline-reinforcement-learning" rel="noopener" target="_blank">https://deepai.org/publication/semi-supervised-reward-learning-for-offline-reinforcement-learning</a> | tldr | 2 |
AGI_and_RL | [
181
] | 1 | 2020-12-23T17:52:46Z | 2020-12-23T17:52:46Z | <b>Mastering Atari, Go, chess and shogi by planning with a learned model</b><br/><a href="http://www.nature.com/articles/s41586-020-03051-4" rel="noopener" target="_blank">www.nature.com/articles/s41586-020-03051-4</a> | tldr | 2 |
AGI_and_RL | [
182
] | 1 | 2020-12-24T13:59:32Z | 2020-12-24T13:59:32Z | <a href="https://github.com/koulanurag/muzero-pytorch" rel="noopener" target="_blank">https://github.com/koulanurag/muzero-pytorch</a> | other | 2 |
AGI_and_RL | [
183
] | 1 | 2021-01-04T04:30:14Z | 2021-01-04T04:30:14Z | Curiosity in exploring chemical space: Intrinsic rewards for deep molecular reinforcement learning <a href="https://arxiv.org/abs/2012.11293" rel="noopener" target="_blank">https://arxiv.org/abs/2012.11293</a> | tldr | 2 |
AGI_and_RL | [
185
] | 1 | 2021-01-05T23:48:51Z | 2021-01-05T23:48:51Z | <b>Multi-agent systems for quadcopters</b> <a href="https://arxiv.org/abs/2101.00033" rel="noopener" target="_blank">https://arxiv.org/abs/2101.00033</a> | tldr | 2 |
AGI_and_RL | [
186
] | 1 | 2021-01-08T13:15:07Z | 2021-01-08T13:15:07Z | A Verified Optimizer for Quantum Circuits <a href="https://www.cs.umd.edu/~mwh/papers/voqc-draft.pdf" rel="noopener" target="_blank">https://www.cs.umd.edu/~mwh/papers/voqc-draft.pdf</a> | tldr | 2 |
AGI_and_RL | [
187
] | 1 | 2021-01-09T04:05:55Z | 2021-01-09T04:05:55Z | Coding for Distributed Multi-Agent Reinforcement Learning <a href="https://arxiv.org/abs/2101.02308" rel="noopener" target="_blank">https://arxiv.org/abs/2101.02308</a> | tldr | 2 |
AGI_and_RL | [
189
] | 1 | 2021-01-15T15:04:02Z | 2021-01-15T15:04:02Z | Prioritized Experience Replay <a href="https://arxiv.org/abs/1511.05952" rel="noopener" target="_blank">https://arxiv.org/abs/1511.05952</a> | tldr | 2 |
AGI_and_RL | [
190
] | 1 | 2021-01-19T14:38:08Z | 2021-01-19T14:38:08Z | Chip Placement with Deep Reinforcement Learning <a href="https://arxiv.org/abs/2004.10746" rel="noopener" target="_blank">https://arxiv.org/abs/2004.10746</a> | tldr | 2 |
AGI_and_RL | [
191
] | 1 | 2021-01-21T20:21:05Z | 2021-01-21T20:21:05Z | Reinforcement learning using chaotic exploration in maze world <a href="https://ieeexplore.ieee.org/document/1491636" rel="noopener" target="_blank">https://ieeexplore.ieee.org/document/1491636</a> | tldr | 2 |
AGI_and_RL | [
192
] | 1 | 2021-01-27T15:24:24Z | 2021-01-27T15:24:24Z | <b>Self-Supervised Reinforcement Learning for Recommender Systems</b> <a href="https://arxiv.org/abs/2006.05779" rel="noopener" target="_blank">https://arxiv.org/abs/2006.05779</a> | tldr | 2 |
AGI_and_RL | [
193
] | 1 | 2021-02-07T11:04:37Z | 2021-02-07T11:04:37Z | <b>Deep Reinforcement Learning Aided Monte Carlo Tree Search for MIMO Detection</b> <a href="https://deepai.org/publication/deep-reinforcement-learning-aided-monte-carlo-tree-search-for-mimo-detection" rel="noopener" target="_blank">https://deepai.org/publication/deep-reinforcement-learning-aided-monte-carlo-tree-searc... | tldr | 2 |
AGI_and_RL | [
194
] | 1 | 2021-02-09T07:04:42Z | 2021-02-09T07:04:42Z | <a href="https://github.com/Z3Prover/z3" rel="noopener" target="_blank">https://github.com/Z3Prover/z3</a> | other | 2 |
AGI_and_RL | [
195
] | 1 | 2021-02-10T03:08:27Z | 2021-02-10T03:08:27Z | <a href="https://botorch.org/" rel="noopener" target="_blank">https://botorch.org/</a> | other | 2 |
AGI_and_RL | [
196
] | 1 | 2021-02-12T05:54:12Z | 2021-02-12T05:54:12Z | <a href="https://github.com/michaelhush/M-LOOP" rel="noopener" target="_blank">https://github.com/michaelhush/M-LOOP</a> | other | 2 |
AGI_and_RL | [
197
] | 1 | 2021-02-16T13:19:19Z | 2021-02-16T13:19:19Z | SAT/SMT by Example <a href="https://sat-smt.codes/SAT_SMT_by_example.pdf" rel="noopener" target="_blank">https://sat-smt.codes/SAT_SMT_by_example.pdf</a> | tldr | 2 |
AGI_and_RL | [
198
] | 1 | 2021-02-16T20:02:58Z | 2021-02-16T20:02:58Z | <a href="https://transporternets.github.io/" rel="noopener" target="_blank">https://transporternets.github.io/</a> | other | 2 |
AGI_and_RL | [
199
] | 1 | 2021-02-24T08:35:16Z | 2021-02-24T08:35:16Z | Graph Neural Networks and Reinforcement Learning for Behavior Generation in Semantic Environments <a href="https://arxiv.org/pdf/2006.12576.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/2006.12576.pdf</a> | tldr | 2 |
AGI_and_RL | [
200
] | 1 | 2021-02-24T09:14:57Z | 2021-02-24T09:14:57Z | NERVENET: LEARNING STRUCTURED POLICY WITH<br/>GRAPH NEURAL NETWORKS <a href="https://openreview.net/pdf?id=S1sqHMZCb" rel="noopener" target="_blank">https://openreview.net/pdf?id=S1sqHMZCb</a> | tldr | 2 |
AGI_and_RL | [
201
] | 1 | 2021-03-10T12:41:31Z | 2021-03-10T12:41:31Z | <b>DeepSynth: Automata Synthesis for Automatic Task Segmentation in Deep Reinforcement Learning</b><br/><a href="https://arxiv.org/abs/1911.10244" rel="noopener" target="_blank">https://arxiv.org/abs/1911.10244</a> | tldr | 2 |
AGI_and_RL | [
202,
203
] | 2 | 2021-03-16T05:18:03Z | 2021-03-16T05:21:02Z | REINFORCED GENETIC ALGORITHM LEARNING FOR<br/>OPTIMIZING COMPUTATION GRAPHS <a href="https://openreview.net/pdf?id=rkxDoJBYPB" rel="noopener" target="_blank">https://openreview.net/pdf?id=rkxDoJBYPB</a>
Reinforcement Learning with Attention that Works: A Self-Supervised Approach <a href="https://arxiv.org/abs/1904.033... | tldr | 2 |
AGI_and_RL | [
204
] | 1 | 2021-03-18T08:28:49Z | 2021-03-18T08:28:49Z | <a href="https://2020blogfor.github.io/posts/2020/04/co/" rel="noopener" target="_blank">https://2020blogfor.github.io/posts/2020/04/co/</a> | other | 2 |
AGI_and_RL | [
206
] | 1 | 2021-03-26T11:54:49Z | 2021-03-26T11:54:49Z | Semi-Supervised Classification with Graph Convolutional Networks <a href="https://arxiv.org/abs/1609.02907" rel="noopener" target="_blank">https://arxiv.org/abs/1609.02907</a> | other | 1 |
AGI_and_RL | [
207
] | 1 | 2021-03-28T15:16:56Z | 2021-03-28T15:16:56Z | <b>Transfer Learning in Deep Reinforcement Learning: A Survey</b> <br/><a href="https://arxiv.org/abs/2009.07888" rel="noopener" target="_blank">https://arxiv.org/abs/2009.07888</a><br/>—-<br/>Reinforcement learning is a learning paradigm for solving sequential decision-making problems. Recent years have witnessed rema... | tldr | 1 |
AGI_and_RL | [
208
] | 1 | 2021-03-29T19:16:08Z | 2021-03-29T19:16:08Z | GIN : High-Performance, Scalable Inference for Graph Neural Networks <a href="http://workshops.inf.ed.ac.uk/accml/papers/2020/AccML_2020_paper_6.pdf" rel="noopener" target="_blank">http://workshops.inf.ed.ac.uk/accml/papers/2020/AccML_2020_paper_6.pdf</a> | tldr | 2 |
AGI_and_RL | [
209
] | 1 | 2021-04-06T14:11:58Z | 2021-04-06T14:11:58Z | Flatland Competition 2020: MAPF and MARL for Efficient Train Coordination on a Grid World <a href="https://arxiv.org/abs/2103.16511" rel="noopener" target="_blank">https://arxiv.org/abs/2103.16511</a> <a href="?q=%23marl">#marl</a> | tldr | 2 |
AGI_and_RL | [
211
] | 1 | 2021-04-15T00:05:53Z | 2021-04-15T00:05:53Z | Mastering Chess and Shogi by Self-Play with a General Reinforcement Learning Algorithm <a href="https://arxiv.org/abs/1712.01815" rel="noopener" target="_blank">https://arxiv.org/abs/1712.01815</a> <a href="?q=%23alphazero">#alphazero</a> | tldr | 2 |
AGI_and_RL | [
212
] | 1 | 2021-04-18T05:26:19Z | 2021-04-18T05:26:19Z | SLATEQ: A Tractable Decomposition for Reinforcement Learning with<br/>Recommendation Sets <a href="https://storage.googleapis.com/pub-tools-public-publication-data/pdf/9f91de1fa0ac351ecb12e4062a37afb896aa1463.pdf" rel="noopener" target="_blank">https://storage.googleapis.com/pub-tools-public-publication-data/pdf/9f91de... | tldr | 2 |
AGI_and_RL | [
213
] | 1 | 2021-04-19T13:07:06Z | 2021-04-19T13:07:06Z | Deep Q-Learning with Q-Matrix Transfer Learning for Novel Fire Evacuation Environment <a href="https://arxiv.org/abs/1905.09673" rel="noopener" target="_blank">https://arxiv.org/abs/1905.09673</a> <a href="?q=%23dqn">#dqn</a> | tldr | 2 |
AGI_and_RL | [
214
] | 1 | 2021-04-20T12:34:52Z | 2021-04-20T12:34:52Z | Computational Foundations of Natural Intelligence <a href="https://www.frontiersin.org/articles/10.3389/fncom.2017.00112/full" rel="noopener" target="_blank">https://www.frontiersin.org/articles/10.3389/fncom.2017.00112/full</a> | other | 1 |
AGI_and_RL | [
215
] | 1 | 2021-04-21T13:05:34Z | 2021-04-21T13:05:34Z | Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification <a href="https://arxiv.org/abs/2103.12656" rel="noopener" target="_blank">https://arxiv.org/abs/2103.12656</a> | tldr | 2 |
AGI_and_RL | [
216
] | 1 | 2021-04-22T15:33:15Z | 2021-04-22T15:33:15Z | Online now: <a href="https://www.youtube.com/watch?v=CjZs5QPBZfk" rel="noopener" target="_blank">https://www.youtube.com/watch?v=CjZs5QPBZfk</a> <a href="?q=%23agi">#agi</a> <a href="?q=%23video">#video</a> | other | 1 |
AGI_and_RL | [
217,
218
] | 2 | 2021-04-24T16:01:14Z | 2021-04-24T16:03:47Z | Russian AGI community: <a href="https://t.me/agirussia" rel="noopener" target="_blank">https://t.me/agirussia</a> <a href="?q=%23community">#community</a>
Reinforcement Learning in a Physics-Inspired Semi-Markov Environment <a href="https://arxiv.org/abs/2004.07333" rel="noopener" target="_blank">https://arxiv.org/abs... | other | 2 |
AGI_and_RL | [
219,
220
] | 2 | 2021-04-26T20:46:12Z | 2021-04-26T20:47:39Z | Connecting Deep-Reinforcement-Learning-based Obstacle Avoidance with Conventional Global Planners using Waypoint Generators <a href="https://arxiv.org/abs/2104.03663" rel="noopener" target="_blank">https://arxiv.org/abs/2104.03663</a>
Multi-Space Evolutionary Search for Large-Scale Optimization <a href="https://arxiv.... | tldr | 2 |
AGI_and_RL | [
221
] | 1 | 2021-05-11T09:46:49Z | 2021-05-11T09:46:49Z | Curiosity-driven Exploration by Self-supervised Prediction <a href="https://arxiv.org/abs/1705.05363" rel="noopener" target="_blank">https://arxiv.org/abs/1705.05363</a> | tldr | 2 |
AGI_and_RL | [
222
] | 1 | 2021-05-12T17:16:18Z | 2021-05-12T17:16:18Z | <a href="https://github.com/optuna/optuna" rel="noopener" target="_blank">https://github.com/optuna/optuna</a> | other | 2 |
AGI_and_RL | [
223
] | 1 | 2021-05-13T06:00:49Z | 2021-05-13T06:00:49Z | Reinforcement Learning and Graph Embedding for Binary Truss Topology Optimization Under Stress and Displacement Constraints <a href="https://www.frontiersin.org/articles/10.3389/fbuil.2020.00059/full" rel="noopener" target="_blank">https://www.frontiersin.org/articles/10.3389/fbuil.2020.00059/full</a> | tldr | 2 |
AGI_and_RL | [
224,
225
] | 2 | 2021-05-13T18:16:44Z | 2021-05-13T18:18:29Z | Bandit based Monte-Carlo Planning <a href="https://sites.ualberta.ca/~szepesva/papers/ecml06.pdf" rel="noopener" target="_blank">https://sites.ualberta.ca/~szepesva/papers/ecml06.pdf</a>
Model Embedding Model-Based Reinforcement Learning <a href="https://arxiv.org/abs/2006.09234" rel="noopener" target="_blank">https:... | tldr | 2 |
AGI_and_RL | [
226
] | 1 | 2021-05-30T20:44:02Z | 2021-05-30T20:44:02Z | Neural Architecture Search with Reinforcement Learning <a href="https://arxiv.org/abs/1611.01578" rel="noopener" target="_blank">https://arxiv.org/abs/1611.01578</a> | tldr | 2 |
AGI_and_RL | [
227
] | 1 | 2021-06-16T23:24:31Z | 2021-06-16T23:24:31Z | Model-Free Learning for Two-Player Zero-Sum Partially Observable Markov Games with Perfect Recall <a href="https://arxiv.org/abs/2106.06279" rel="noopener" target="_blank">https://arxiv.org/abs/2106.06279</a> | tldr | 2 |
AGI_and_RL | [
228
] | 1 | 2021-06-19T15:42:44Z | 2021-06-19T15:42:44Z | The world as a neural network <a href="https://arxiv.org/abs/2008.01540" rel="noopener" target="_blank">https://arxiv.org/abs/2008.01540</a> | tldr | 2 |
AGI_and_RL | [
230
] | 1 | 2021-07-14T10:27:52Z | 2021-07-14T10:27:52Z | Reducing the Computational Cost of Deep Reinforcement Learning Research <a href="https://ai.googleblog.com/2021/07/reducing-computational-cost-of-deep.html?m=1" rel="noopener" target="_blank">https://ai.googleblog.com/2021/07/reducing-computational-cost-of-deep.html?m=1</a> | tldr | 2 |
AGI_and_RL | [
231
] | 1 | 2021-07-20T03:57:49Z | 2021-07-20T03:57:49Z | [2107.08170] Megaverse: Simulating Embodied Agents at One Million Experiences per Second<br/><a href="https://arxiv.org/abs/2107.08170" rel="noopener" target="_blank">https://arxiv.org/abs/2107.08170</a> | other | 1 |
AGI_and_RL | [
233
] | 1 | 2021-07-25T15:40:26Z | 2021-07-25T15:40:26Z | MarsExplorer: Exploration of Unknown Terrains via Deep Reinforcement Learning and Procedurally Generated Environments | DeepAI<br/><a href="https://deepai.org/publication/marsexplorer-exploration-of-unknown-terrains-via-deep-reinforcement-learning-and-procedurally-generated-environments" rel="noopener" target="_blank">... | tldr | 2 |
AGI_and_RL | [
234
] | 1 | 2021-07-27T18:57:23Z | 2021-07-27T18:57:23Z | <a href="https://github.com/tensorflow/agents" rel="noopener" target="_blank">https://github.com/tensorflow/agents</a> | other | 2 |
AGI_and_RL | [
235
] | 1 | 2021-07-28T01:34:44Z | 2021-07-28T01:34:44Z | <a href="https://github.com/deepmind/lab" rel="noopener" target="_blank">https://github.com/deepmind/lab</a> | other | 1 |
AGI_and_RL | [
236
] | 1 | 2021-08-02T02:43:52Z | 2021-08-02T02:43:52Z | - Survey of Recent Multi-Agent Reinforcement Learning Algorithms Utilizing Centralized Training. (arXiv:2107.14316v1 [<a href="http://cs.MA/" rel="noopener" target="_blank">cs.MA</a>])<br/> <a href="http://arxiv.org/abs/2107.14316" rel="noopener" target="_blank">http://arxiv.org/abs/2107.14316</a> | other | 1 |
AGI_and_RL | [
237,
238
] | 2 | 2021-08-02T04:24:54Z | 2021-08-02T04:25:34Z | [2107.14698] Strategically Efficient Exploration in Competitive Multi-agent Reinforcement Learning<br/><a href="https://arxiv.org/abs/2107.14698" rel="noopener" target="_blank">https://arxiv.org/abs/2107.14698</a>
GitHub - microsoft/strategically_efficient_rl: More efficient exploration for reinforcement learning in t... | other | 2 |
AGI_and_RL | [
239
] | 1 | 2021-08-02T11:03:54Z | 2021-08-02T11:03:54Z | <b>Model-based Reinforcement Learning: A Survey</b> <br/><a href="https://arxiv.org/abs/2006.16712" rel="noopener" target="_blank">https://arxiv.org/abs/2006.16712</a><br/>—-<br/>Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is a important challenge in artificial intelli... | tldr | 2 |
AGI_and_RL | [
240
] | 1 | 2021-08-03T13:58:14Z | 2021-08-03T13:58:14Z | Hindsight Experience Replay <a href="https://arxiv.org/abs/1707.01495" rel="noopener" target="_blank">https://arxiv.org/abs/1707.01495</a> | tldr | 2 |
AGI_and_RL | [
241
] | 1 | 2021-08-04T08:11:37Z | 2021-08-04T08:11:37Z | GitHub - facebookresearch/mtenv: MultiTask Environments for Reinforcement Learning.<br/><a href="https://github.com/facebookresearch/mtenv" rel="noopener" target="_blank">https://github.com/facebookresearch/mtenv</a> | other | 2 |
AGI_and_RL | [
245
] | 1 | 2021-08-12T15:19:30Z | 2021-08-12T15:19:30Z | <a href="https://github.com/jack-willturner/gymnastics" rel="noopener" target="_blank">https://github.com/jack-willturner/gymnastics</a> | other | 2 |
AGI_and_RL | [
246,
247,
248,
249
] | 4 | 2021-08-14T01:51:03Z | 2021-08-14T02:19:59Z | GAN Q-learning <a href="https://deepai.org/publication/gan-q-learning" rel="noopener" target="_blank">https://deepai.org/publication/gan-q-learning</a>
<a href="https://github.com/louaaron/GAN-Q-Learning" rel="noopener" target="_blank">https://github.com/louaaron/GAN-Q-Learning</a>
GAN-powered Deep Distributional Rei... | other | 2 |
AGI_and_RL | [
250
] | 1 | 2021-08-16T11:22:23Z | 2021-08-16T11:22:23Z | Dealing with Sparse Rewards in Reinforcement Learning <a href="https://arxiv.org/abs/1910.09281" rel="noopener" target="_blank">https://arxiv.org/abs/1910.09281</a> | tldr | 2 |
AGI_and_RL | [
251
] | 1 | 2021-08-16T11:59:28Z | 2021-08-16T11:59:28Z | <a href="https://github.com/CoderOneHQ/ultimate-volleyball" rel="noopener" target="_blank">https://github.com/CoderOneHQ/ultimate-volleyball</a> | other | 2 |
AGI_and_RL | [
252,
253
] | 2 | 2021-08-18T02:23:11Z | 2021-08-18T02:23:20Z | Revisiting State Augmentation methods for Reinforcement Learning with Stochastic Delays <a href="https://arxiv.org/abs/2108.07555" rel="noopener" target="_blank">https://arxiv.org/abs/2108.07555</a>
Revocable Deep Reinforcement Learning with Affinity Regularization for Outlier-Robust Graph Matching <a href="https://ar... | tldr | 2 |
AGI_and_RL | [
254,
255
] | 2 | 2021-08-24T11:50:11Z | 2021-08-24T11:50:58Z | Program Synthesis with Large Language Models <a href="https://arxiv.org/abs/2108.07732" rel="noopener" target="_blank">https://arxiv.org/abs/2108.07732</a>
Plug and Play, Model-Based Reinforcement Learning <a href="https://deepai.org/publication/plug-and-play-model-based-reinforcement-learning" rel="noopener" target="... | tldr | 2 |
AGI_and_RL | [
257
] | 1 | 2021-08-27T01:46:16Z | 2021-08-27T01:46:16Z | - From Statistical Relational to Neural Symbolic Artificial Intelligence: a Survey. (arXiv:2108.11451v1 [<a href="http://cs.AI/" rel="noopener" target="_blank">cs.AI</a>])<br/> <a href="http://arxiv.org/abs/2108.11451" rel="noopener" target="_blank">http://arxiv.org/abs/2108.11451</a> | tldr | 2 |
AGI_and_RL | [
258,
259
] | 2 | 2021-08-27T13:56:54Z | 2021-08-27T14:01:04Z | Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning <a href="https://arxiv.org/abs/2108.10470" rel="noopener" target="_blank">https://arxiv.org/abs/2108.10470</a>
<a href="https://github.com/robotlearn/pyrobolearn" rel="noopener" target="_blank">https://github.com/robotlearn/pyrobolearn</a> | other | 2 |
AGI_and_RL | [
260
] | 1 | 2021-08-27T19:42:26Z | 2021-08-27T19:42:26Z | <a href="http://higcompetition.info/" rel="noopener" target="_blank">http://higcompetition.info/</a> | other | 2 |
AGI_and_RL | [
261
] | 1 | 2021-08-28T14:00:10Z | 2021-08-28T14:00:10Z | [2108.11670] Quantum Alphatron<br/><a href="https://arxiv.org/abs/2108.11670" rel="noopener" target="_blank">https://arxiv.org/abs/2108.11670</a> | tldr | 2 |
AGI_and_RL | [
262
] | 1 | 2021-08-31T02:21:57Z | 2021-08-31T02:21:57Z | - ReGen: Reinforcement Learning for Text and Knowledge Base Generation using Pretrained Language Models. (arXiv:2108.12472v1 [<a href="http://cs.CL/" rel="noopener" target="_blank">cs.CL</a>])<br/> <a href="http://arxiv.org/abs/2108.12472" rel="noopener" target="_blank">http://arxiv.org/abs/2108.12472</a> | tldr | 2 |
AGI_and_RL | [
263,
264,
265
] | 3 | 2021-09-06T05:33:57Z | 2021-09-06T05:36:54Z | - Multi-Agent Inverse Reinforcement Learning: Suboptimal Demonstrations and Alternative Solution Concepts. (arXiv:2109.01178v1 [<a href="http://cs.AI/" rel="noopener" target="_blank">cs.AI</a>])<br/> <a href="http://arxiv.org/abs/2109.01178" rel="noopener" target="_blank">http://arxiv.org/abs/2109.01178</a>
Provably S... | other | 2 |
AGI_and_RL | [
267
] | 1 | 2021-11-04T17:42:59Z | 2021-11-04T17:42:59Z | <a href="https://deepmind.com/learning-resources/reinforcement-learning-series-2021" rel="noopener" target="_blank">https://deepmind.com/learning-resources/reinforcement-learning-series-2021</a> | other | 2 |
AGI_and_RL | [
268
] | 1 | 2021-11-09T20:06:30Z | 2021-11-09T20:06:30Z | When Cyber-Physical Systems Meet AI: A Benchmark, an Evaluation, and a Way Forward <a href="https://arxiv.org/abs/2111.04324" rel="noopener" target="_blank">https://arxiv.org/abs/2111.04324</a> | tldr | 2 |
AGI_and_RL | [
269
] | 1 | 2021-11-10T03:44:25Z | 2021-11-10T03:44:25Z | Understanding the Effects of Dataset Characteristics on Offline Reinforcement Learning <a href="https://arxiv.org/abs/2111.04714" rel="noopener" target="_blank">https://arxiv.org/abs/2111.04714</a> | tldr | 2 |
AGI_and_RL | [
270,
271
] | 2 | 2021-11-18T14:58:08Z | 2021-11-18T14:58:13Z | Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts <a href="https://arxiv.org/abs/2105.03363" rel="noopener" target="_blank">https://arxiv.org/abs/2105.03363</a>
<a href="https://www.youtube.com/watch?v=aD5aONXrYzA" rel="noopener" target="_blank">https://www.youtube.com/watch?v=aD5aONXrYz... | other | 2 |
AGI_and_RL | [
272
] | 1 | 2021-11-20T18:09:32Z | 2021-11-20T18:09:32Z | <a href="https://www.youtube.com/watch?v=ELE2_Mftqoc" rel="noopener" target="_blank">https://www.youtube.com/watch?v=ELE2_Mftqoc</a> | other | 2 |
AGI_and_RL | [
273
] | 1 | 2021-11-25T01:01:05Z | 2021-11-25T01:01:05Z | Beyond Fine-Tuning: Transferring Behavior in Reinforcement Learning <a href="https://arxiv.org/abs/2102.13515" rel="noopener" target="_blank">https://arxiv.org/abs/2102.13515</a> | tldr | 2 |
AGI_and_RL | [
274
] | 1 | 2021-12-01T17:48:50Z | 2021-12-01T17:48:50Z | <a href="https://deepmind.com/blog/article/exploring-the-beauty-of-pure-mathematics-in-novel-ways" rel="noopener" target="_blank">https://deepmind.com/blog/article/exploring-the-beauty-of-pure-mathematics-in-novel-ways</a> | tldr | 2 |
AGI_and_RL | [
275,
276
] | 2 | 2021-12-13T19:21:06Z | 2021-12-13T19:21:43Z | <a href="https://www.pnas.org/content/117/4/1853" rel="noopener" target="_blank">https://www.pnas.org/content/117/4/1853</a>
<a href="https://www.pnas.org/content/118/49/e2112672118" rel="noopener" target="_blank">https://www.pnas.org/content/118/49/e2112672118</a> | tldr | 2 |
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