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 | [
2
] | 1 | 2020-01-31T14:59:09 | 2020-01-31T14:59:09 | <a href="https://arxiv.org/abs/1912.01513" rel="noopener" target="_blank">https://arxiv.org/abs/1912.01513</a> | other | 1 |
AGI_and_RL | [
3,
4,
5
] | 3 | 2020-02-02T16:47:01 | 2020-02-02T16:58:06 | it's one of most important paper in RL: <a href="https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf" rel="noopener" target="_blank">https://www.cs.toronto.edu/~vmnih/docs/dqn.pdf</a>
Scalable RL using RLLib: <a href="https://ray.readthedocs.io/en/latest/rllib.html" rel="noopener" target="_blank">https://ray.readthedocs.i... | other | 1 |
AGI_and_RL | [
6
] | 1 | 2020-02-02T22:58:21 | 2020-02-02T22:58:21 | <a href="https://bair.berkeley.edu/blog/" rel="noopener" target="_blank">https://bair.berkeley.edu/blog/</a> | other | 1 |
AGI_and_RL | [
7
] | 1 | 2020-02-04T17:12:28 | 2020-02-04T17:12:28 | Neural MMO v1.3: A Massively Multiagent Game Environment for Training and Evaluating Neural Networks <br/><a href="https://arxiv.org/abs/2001.12004" rel="noopener" target="_blank">https://arxiv.org/abs/2001.12004</a> | other | 1 |
AGI_and_RL | [
8,
9
] | 2 | 2020-02-04T19:44:05 | 2020-02-04T19:46:18 | Free Online book about RL algorithms: <a href="https://sites.ualberta.ca/~szepesva/RLBook.html" rel="noopener" target="_blank">https://sites.ualberta.ca/~szepesva/RLBook.html</a>
Reinforcement Learning: An Introduction <a href="http://incompleteideas.net/book/the-book-2nd.html" rel="noopener" target="_blank">http://in... | other | 1 |
AGI_and_RL | [
10
] | 1 | 2020-02-06T19:51:43 | 2020-02-06T19:51:43 | Bootstrapping a DQN Replay Memory with<br/>Synthetic Experiences [04.02.2020] <a href="https://arxiv.org/pdf/2002.01370.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/2002.01370.pdf</a> | other | 1 |
AGI_and_RL | [
11
] | 1 | 2020-02-15T07:15:13 | 2020-02-15T07:15:13 | Cool RL course by Yandex: <a href="https://github.com/yandexdataschool/Practical_RL" rel="noopener" target="_blank">https://github.com/yandexdataschool/Practical_RL</a> | other | 1 |
AGI_and_RL | [
12
] | 1 | 2020-02-15T19:17:38 | 2020-02-15T19:17:38 | There is something about MDP: <a href="http://www.cs.cmu.edu/afs/cs/academic/class/15780-s16/www/slides/mdps.pdf" rel="noopener" target="_blank">http://www.cs.cmu.edu/afs/cs/academic/class/15780-s16/www/slides/mdps.pdf</a> | other | 1 |
AGI_and_RL | [
13
] | 1 | 2020-02-20T14:09:24 | 2020-02-20T14:09:24 | Value-driven Hindsight Modelling <a href="https://arxiv.org/pdf/2002.08329.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/2002.08329.pdf</a> | other | 1 |
AGI_and_RL | [
14
] | 1 | 2020-02-20T22:27:17 | 2020-02-20T22:27:17 | In this rep I found some interesting RL <a href="https://github.com/RaviVijay/DeepLearningEE" rel="noopener" target="_blank">https://github.com/RaviVijay/DeepLearningEE</a> | other | 1 |
AGI_and_RL | [
15
] | 1 | 2020-03-10T06:47:24 | 2020-03-10T06:47:24 | Awesome course from Berkeley: <a href="http://ai.berkeley.edu/reinforcement.html" rel="noopener" target="_blank">http://ai.berkeley.edu/reinforcement.html</a> | other | 1 |
AGI_and_RL | [
16
] | 1 | 2020-03-19T13:20:20 | 2020-03-19T13:20:20 | <a href="https://attentionagent.github.io/" rel="noopener" target="_blank">https://attentionagent.github.io/</a> | other | 1 |
AGI_and_RL | [
18
] | 1 | 2020-03-19T20:14:01 | 2020-03-19T20:14:01 | <a href="https://ai.stanford.edu/blog/modeling-risky-humans/" rel="noopener" target="_blank">https://ai.stanford.edu/blog/modeling-risky-humans/</a> | other | 1 |
AGI_and_RL | [
19
] | 1 | 2020-03-22T02:00:54 | 2020-03-22T02:00:54 | <a href="https://towardsdatascience.com/cross-entropy-method-for-reinforcement-learning-2b6de2a4f3a0" rel="noopener" target="_blank">https://towardsdatascience.com/cross-entropy-method-for-reinforcement-learning-2b6de2a4f3a0</a> | other | 1 |
AGI_and_RL | [
20
] | 1 | 2020-03-24T18:45:34 | 2020-03-24T18:45:34 | <a href="https://ai.googleblog.com/2020/03/massively-scaling-reinforcement.html?m=1" rel="noopener" target="_blank">https://ai.googleblog.com/2020/03/massively-scaling-reinforcement.html?m=1</a> | other | 1 |
AGI_and_RL | [
21,
22
] | 2 | 2020-04-07T11:07:51 | 2020-04-07T11:08:02 | Weakly-Supervised Reinforcement Learning for Controllable Behavior <a href="https://arxiv.org/abs/2004.02860" rel="noopener" target="_blank">https://arxiv.org/abs/2004.02860</a>
Using Multi-Agent Reinforcement Learning in Auction Simulations <a href="https://arxiv.org/abs/2004.02764" rel="noopener" target="_blank">htt... | other | 1 |
AGI_and_RL | [
23
] | 1 | 2020-04-18T07:26:10 | 2020-04-18T07:26:10 | <a href="https://medium.com/analytics-vidhya/building-a-powerful-dqn-in-tensorflow-2-0-explanation-tutorial-d48ea8f3177a" rel="noopener" target="_blank">https://medium.com/analytics-vidhya/building-a-powerful-dqn-in-tensorflow-2-0-explanation-tutorial-d48ea8f3177a</a> | other | 1 |
AGI_and_RL | [
24
] | 1 | 2020-04-19T17:49:06 | 2020-04-19T17:49:06 | Deep Reinforcement Learning in Fluid Mechanics: a promising method for both Active Flow Control and Shape Optimization <a href="https://arxiv.org/abs/2001.02464" rel="noopener" target="_blank">https://arxiv.org/abs/2001.02464</a> | other | 1 |
AGI_and_RL | [
25
] | 1 | 2020-04-21T12:13:46 | 2020-04-21T12:13:46 | There is something interesting! Model-based actor-critic: GAN + DRL (actor-critic) => AGI<br/><a href="https://arxiv.org/abs/2004.04574" rel="noopener" target="_blank">https://arxiv.org/abs/2004.04574</a> | other | 1 |
AGI_and_RL | [
26
] | 1 | 2020-04-24T16:23:42 | 2020-04-24T16:23:42 | <a href="http://heli.stanford.edu/" rel="noopener" target="_blank">http://heli.stanford.edu/</a> | other | 1 |
AGI_and_RL | [
27
] | 1 | 2020-04-26T22:03:06 | 2020-04-26T22:03:06 | <a href="https://www.eigensteve.com/" rel="noopener" target="_blank">https://www.eigensteve.com/</a> | other | 1 |
AGI_and_RL | [
28,
29
] | 2 | 2020-04-28T09:04:10 | 2020-04-28T09:11:13 | Sim2Real Transfer for Reinforcement Learning without Dynamics Randomization <a href="https://arxiv.org/abs/2002.11635" rel="noopener" target="_blank">https://arxiv.org/abs/2002.11635</a>
<a href="https://sim2realai.github.io/Synthetic-Datasets-of-Objects-Part-I/" rel="noopener" target="_blank">https://sim2realai.githu... | other | 1 |
AGI_and_RL | [
30
] | 1 | 2020-04-28T21:57:19 | 2020-04-28T21:57:19 | <a href="https://pathmind.com/wiki/deep-reinforcement-learning" rel="noopener" target="_blank">https://pathmind.com/wiki/deep-reinforcement-learning</a> | other | 1 |
AGI_and_RL | [
31
] | 1 | 2020-04-29T22:23:49 | 2020-04-29T22:23:49 | <a href="http://proceedings.mlr.press/v97/chen19c.html" rel="noopener" target="_blank">http://proceedings.mlr.press/v97/chen19c.html</a> | other | 1 |
AGI_and_RL | [
32
] | 1 | 2020-05-02T22:31:46 | 2020-05-02T22:31:46 | AutoML-Zero: Evolving Machine Learning Algorithms From Scratch<br/> <a href="https://arxiv.org/pdf/2003.03384.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/2003.03384.pdf</a> | other | 1 |
AGI_and_RL | [
33
] | 1 | 2020-05-03T00:20:09 | 2020-05-03T00:20:09 | <a href="https://github.com/google-research/google-research" rel="noopener" target="_blank">https://github.com/google-research/google-research</a> | other | 1 |
AGI_and_RL | [
34
] | 1 | 2020-05-03T07:15:48 | 2020-05-03T07:15:48 | <a href="https://github.com/jobtalle/Cephalopods" rel="noopener" target="_blank">https://github.com/jobtalle/Cephalopods</a> | other | 1 |
AGI_and_RL | [
35,
36
] | 2 | 2020-05-04T01:18:47 | 2020-05-04T01:20:38 | <a href="https://github.com/facebookresearch/habitat-sim" rel="noopener" target="_blank">https://github.com/facebookresearch/habitat-sim</a>
<a href="http://gibsonenv.stanford.edu/database/" rel="noopener" target="_blank">http://gibsonenv.stanford.edu/database/</a> | other | 1 |
AGI_and_RL | [
37
] | 1 | 2020-05-05T05:04:10 | 2020-05-05T05:04:10 | <a href="https://iclr.cc/virtual_2020/papers.html?filter=keywords" rel="noopener" target="_blank">https://iclr.cc/virtual_2020/papers.html?filter=keywords</a> | other | 1 |
AGI_and_RL | [
39
] | 1 | 2020-05-08T14:58:29 | 2020-05-08T14:58:29 | <a href="http://burlap.cs.brown.edu/" rel="noopener" target="_blank">http://burlap.cs.brown.edu/</a> | other | 1 |
AGI_and_RL | [
40
] | 1 | 2020-05-09T10:35:58 | 2020-05-09T10:35:58 | Reinforcement Learning Algorithms with Python: Learn, understand, and develop smart algorithms for addressing AI challenges [2019] Andrea Lonza<br/><br/>Reinforcement Learning (RL) is a popular and promising branch of AI that involves making smarter models and agents that can automatically determine ideal behavior base... | tldr | 1 |
AGI_and_RL | [
41
] | 1 | 2020-05-10T13:59:40 | 2020-05-10T13:59:40 | Adversarial Policies <a href="https://arxiv.org/pdf/1905.10615.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/1905.10615.pdf</a> | other | 1 |
AGI_and_RL | [
42
] | 1 | 2020-05-10T16:07:56 | 2020-05-10T16:07:56 | <a href="https://github.com/ray-project/ray" rel="noopener" target="_blank">https://github.com/ray-project/ray</a> | other | 1 |
AGI_and_RL | [
43
] | 1 | 2020-05-12T22:50:49 | 2020-05-12T22:50:49 | <a href="https://bayesgroup.github.io/tqc/" rel="noopener" target="_blank">https://bayesgroup.github.io/tqc/</a> | other | 1 |
AGI_and_RL | [
44
] | 1 | 2020-05-13T00:39:57 | 2020-05-13T00:39:57 | <a href="https://eng.uber.com/enhanced-poet-machine-learning/" rel="noopener" target="_blank">https://eng.uber.com/enhanced-poet-machine-learning/</a> | other | 1 |
AGI_and_RL | [
45
] | 1 | 2020-05-13T21:52:25 | 2020-05-13T21:52:25 | <a href="http://www.wildml.com/2016/10/learning-reinforcement-learning/" rel="noopener" target="_blank">http://www.wildml.com/2016/10/learning-reinforcement-learning/</a> | other | 1 |
AGI_and_RL | [
46
] | 1 | 2020-05-14T22:01:59 | 2020-05-14T22:01:59 | <a href="https://geometric-relational-dl.github.io/" rel="noopener" target="_blank">https://geometric-relational-dl.github.io/</a> | other | 1 |
AGI_and_RL | [
47
] | 1 | 2020-05-17T03:33:58 | 2020-05-17T03:33:58 | <b>A Survey on Practical Applications of Multi-Armed and Contextual Bandits</b> <br/><a href="https://arxiv.org/abs/1904.10040" rel="noopener" target="_blank">https://arxiv.org/abs/1904.10040</a><br/>—-<br/>In recent years, multi-armed bandit (MAB) framework has attracted a lot of attention in various applications, fro... | tldr | 1 |
AGI_and_RL | [
48
] | 1 | 2020-05-17T19:44:55 | 2020-05-17T19:44:55 | <a href="https://github.com/adityathakker/awesome-rl-nlp" rel="noopener" target="_blank">https://github.com/adityathakker/awesome-rl-nlp</a> | other | 1 |
AGI_and_RL | [
49
] | 1 | 2020-05-19T00:07:30 | 2020-05-19T00:07:30 | <a href="https://github.com/Khrylx/PyTorch-RL" rel="noopener" target="_blank">https://github.com/Khrylx/PyTorch-RL</a> | other | 1 |
AGI_and_RL | [
51,
50
] | 2 | 2020-05-19T14:53:12 | 2020-05-19T14:53:12 | <a href="https://github.com/ugurkanates/MLAgents-Google-Collab" rel="noopener" target="_blank">https://github.com/ugurkanates/MLAgents-Google-Collab</a> <br/><br/>I have created a Rainbow DQN notebook with Categorical features that works & shows how to manipulate Unity ML Agents environment in their newyl released ... | other | 1 |
AGI_and_RL | [
52,
53
] | 2 | 2020-05-24T17:48:07 | 2020-05-24T17:48:12 | <a href="https://github.com/saeed349/Deep-Reinforcement-Learning-in-Trading" rel="noopener" target="_blank">https://github.com/saeed349/Deep-Reinforcement-Learning-in-Trading</a>
<a href="https://github.com/edwardhdlu/q-trader" rel="noopener" target="_blank">https://github.com/edwardhdlu/q-trader</a> | other | 1 |
AGI_and_RL | [
54
] | 1 | 2020-05-26T07:58:36 | 2020-05-26T07:58:36 | Reinforcement Learning: An Introduction, second edition, 2020<br/>Richard S. Sutton and Andrew G. Barto<br/><a href="http://incompleteideas.net/book/RLbook2020.pdf" rel="noopener" target="_blank">http://incompleteideas.net/book/RLbook2020.pdf</a> | other | 1 |
AGI_and_RL | [
55
] | 1 | 2020-05-26T08:29:00 | 2020-05-26T08:29:00 | Some classic: Proximal Policy Optimization Algorithms<br/><a href="https://arxiv.org/pdf/1707.06347.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/1707.06347.pdf</a> | other | 1 |
AGI_and_RL | [
56
] | 1 | 2020-05-27T01:28:24 | 2020-05-27T01:28:24 | Monte Carlo Gradient Estimation in Machine Learning <a href="https://arxiv.org/pdf/1906.10652.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/1906.10652.pdf</a> | other | 1 |
AGI_and_RL | [
57
] | 1 | 2020-05-30T16:08:39 | 2020-05-30T16:08:39 | <a href="https://offline-rl.github.io/" rel="noopener" target="_blank">https://offline-rl.github.io/</a> | other | 1 |
AGI_and_RL | [
58
] | 1 | 2020-05-30T21:31:35 | 2020-05-30T21:31:35 | Ceres Solver: <a href="http://ceres-solver.org/index.html" rel="noopener" target="_blank">http://ceres-solver.org/index.html</a> | other | 1 |
AGI_and_RL | [
60,
61
] | 2 | 2020-06-04T18:52:57 | 2020-06-04T18:54:29 | <a href="https://blog.tensorflow.org/" rel="noopener" target="_blank">https://blog.tensorflow.org/</a>
Learning Memory-Based Control for Human-Scale Bipedal Locomotion: <a href="https://arxiv.org/abs/2006.02402" rel="noopener" target="_blank">https://arxiv.org/abs/2006.02402</a> | other | 1 |
AGI_and_RL | [
62
] | 1 | 2020-06-06T13:03:34 | 2020-06-06T13:03:34 | <b>Meta-Model-Based Meta-Policy Optimization</b> <a href="https://arxiv.org/abs/2006.02608" rel="noopener" target="_blank">https://arxiv.org/abs/2006.02608</a> | other | 1 |
AGI_and_RL | [
63,
64
] | 2 | 2020-06-08T14:02:54 | 2020-06-08T14:03:21 | Google Research Football: A Novel Reinforcement Learning Environment <a href="https://arxiv.org/abs/1907.11180" rel="noopener" target="_blank">https://arxiv.org/abs/1907.11180</a>
<a href="https://ai.googleblog.com/2019/06/introducing-google-research-football.html" rel="noopener" target="_blank">https://ai.googleblog.... | other | 1 |
AGI_and_RL | [
65,
66
] | 2 | 2020-06-11T19:28:29 | 2020-06-11T19:29:45 | <a href="https://github.com/aqeelanwar/PEDRA" rel="noopener" target="_blank">https://github.com/aqeelanwar/PEDRA</a>
<a href="https://github.com/aarg-kcis/minion-ros-gazeobo-rviz" rel="noopener" target="_blank">https://github.com/aarg-kcis/minion-ros-gazeobo-rviz</a> | other | 1 |
AGI_and_RL | [
67
] | 1 | 2020-06-12T19:04:09 | 2020-06-12T19:04:09 | Soft Actor-Critic Algorithms and Applications <a href="https://arxiv.org/abs/1812.05905" rel="noopener" target="_blank">https://arxiv.org/abs/1812.05905</a> | other | 1 |
AGI_and_RL | [
68,
69
] | 2 | 2020-06-13T09:11:27 | 2020-06-13T09:13:25 | Task-Relevant Adversarial Imitation Learning <a href="https://arxiv.org/abs/1910.01077" rel="noopener" target="_blank">https://arxiv.org/abs/1910.01077</a>
Reinforcement Learning for Molecular Design Guided by Quantum Mechanics <a href="https://arxiv.org/abs/2002.07717" rel="noopener" target="_blank">https://arxiv.org... | other | 1 |
AGI_and_RL | [
70
] | 1 | 2020-06-15T01:17:19 | 2020-06-15T01:17:19 | <b>Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation</b><br/><br/><a href="https://arxiv.org/abs/2006.06135" rel="noopener" target="_blank">https://arxiv.org/abs/2006.06135</a> | other | 1 |
AGI_and_RL | [
71
] | 1 | 2020-06-19T08:49:46 | 2020-06-19T08:49:46 | Multi-Agent Reinforcement Learning:<br/>A Selective Overview of Theories and Algorithms <a href="https://arxiv.org/pdf/1911.10635.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/1911.10635.pdf</a> | other | 1 |
AGI_and_RL | [
72,
73,
74
] | 3 | 2020-06-22T10:10:32 | 2020-06-22T10:33:25 | <a href="https://connect.unity.com/challenges/ml-agents-1" rel="noopener" target="_blank">https://connect.unity.com/challenges/ml-agents-1</a>
<a href="https://github.com/LantaoYu/MARL-Papers" rel="noopener" target="_blank">https://github.com/LantaoYu/MARL-Papers</a>
<a href="http://carla.org/" rel="noopener" target=... | other | 1 |
AGI_and_RL | [
75
] | 1 | 2020-06-23T11:50:04 | 2020-06-23T11:50:04 | <a href="https://github.com/stepjam/RLBench" rel="noopener" target="_blank">https://github.com/stepjam/RLBench</a> | other | 1 |
AGI_and_RL | [
76
] | 1 | 2020-06-25T17:10:25 | 2020-06-25T17:10:25 | <a href="https://pathak22.github.io/modular-assemblies/" rel="noopener" target="_blank">https://pathak22.github.io/modular-assemblies/</a> | other | 1 |
AGI_and_RL | [
77
] | 1 | 2020-06-30T04:30:51 | 2020-06-30T04:30:51 | <a href="https://github.com/ugurkanates/awesome-real-world-rl" rel="noopener" target="_blank">https://github.com/ugurkanates/awesome-real-world-rl</a> | other | 1 |
AGI_and_RL | [
78
] | 1 | 2020-07-01T15:38:22 | 2020-07-01T15:38:22 | Hello, subscribers) I'm glad to present you a JavaScript reinforcement learning library: <a href="https://github.com/polyzer/rllib.js" rel="noopener" target="_blank">https://github.com/polyzer/rllib.js</a> Description is in the repository. Now the development is only at the initial stage, but there are big plans) | other | 1 |
AGI_and_RL | [
79
] | 1 | 2020-07-01T17:31:26 | 2020-07-01T17:31:26 | Evaluating the Performance of Reinforcement Learning Algorithms <a href="https://arxiv.org/pdf/2006.16958.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/2006.16958.pdf</a> | other | 1 |
AGI_and_RL | [
81
] | 1 | 2020-07-20T12:20:18 | 2020-07-20T12:20:18 | Using Fractal Neural Networks to Play SimCity 1 and Conway's Game of Life at Variable Scales <a href="https://arxiv.org/abs/2002.03896" rel="noopener" target="_blank">https://arxiv.org/abs/2002.03896</a> | other | 1 |
AGI_and_RL | [
82
] | 1 | 2020-07-24T07:37:28 | 2020-07-24T07:37:28 | <a href="https://venturebeat.com/2020/07/20/deepminds-ai-automatically-generates-reinforcement-learning-algorithms/" rel="noopener" target="_blank">https://venturebeat.com/2020/07/20/deepminds-ai-automatically-generates-reinforcement-learning-algorithms/</a> | other | 1 |
AGI_and_RL | [
83
] | 1 | 2020-08-13T06:56:29 | 2020-08-13T06:56:29 | <a href="https://ai.googleblog.com/2020/08/a-simulation-suite-for-tackling-applied.html?m=1" rel="noopener" target="_blank">https://ai.googleblog.com/2020/08/a-simulation-suite-for-tackling-applied.html?m=1</a> | other | 1 |
AGI_and_RL | [
84
] | 1 | 2020-08-14T10:03:52 | 2020-08-14T10:03:52 | DISTRIBUTED DISTRIBUTIONAL DETERMINISTIC<br/>POLICY GRADIENTS: <a href="https://arxiv.org/pdf/1804.08617.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/1804.08617.pdf</a> | other | 1 |
AGI_and_RL | [
85
] | 1 | 2020-08-28T16:44:00 | 2020-08-28T16:44:00 | <a href="https://ai.googleblog.com/2020/08/tackling-open-challenges-in-offline.html" rel="noopener" target="_blank">https://ai.googleblog.com/2020/08/tackling-open-challenges-in-offline.html</a> | other | 1 |
AGI_and_RL | [
86
] | 1 | 2020-09-02T09:43:10 | 2020-09-02T09:43:10 | Hello, everyone) I work on rllib.js improvement. You can check updates: <a href="https://github.com/polyzer/rllib.js" rel="noopener" target="_blank">https://github.com/polyzer/rllib.js</a> | other | 1 |
AGI_and_RL | [
87
] | 1 | 2020-09-04T14:45:24 | 2020-09-04T14:45:24 | Grounded Language Learning Fast and Slow <a href="https://arxiv.org/pdf/2009.01719.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/2009.01719.pdf</a> | other | 1 |
AGI_and_RL | [
88,
89
] | 2 | 2020-09-17T12:16:48 | 2020-09-17T12:20:44 | <a href="https://ai.googleblog.com/2020/09/imitation-learning-in-low-data-regime.html" rel="noopener" target="_blank">https://ai.googleblog.com/2020/09/imitation-learning-in-low-data-regime.html</a>
An Algorithmic Perspective on<br/>Imitation Learning <a href="https://arxiv.org/pdf/1811.06711.pdf" rel="noopener" targe... | other | 1 |
AGI_and_RL | [
90
] | 1 | 2020-09-19T15:07:14 | 2020-09-19T15:07:14 | Deep Reinforcement Learning for Unknown Anomaly<br/>Detection <a href="https://arxiv.org/pdf/2009.06847v1.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/2009.06847v1.pdf</a> | other | 1 |
AGI_and_RL | [
91
] | 1 | 2020-09-25T05:11:31 | 2020-09-25T05:11:31 | <a href="https://twitter.com/arXiv_Daily/status/1309359300796542977?s=09" rel="noopener" target="_blank">https://twitter.com/arXiv_Daily/status/1309359300796542977?s=09</a> | other | 1 |
AGI_and_RL | [
92
] | 1 | 2020-10-01T08:56:32 | 2020-10-01T08:56:32 | <a href="https://twitter.com/arxiv_org/status/1311354903680360449?s=09" rel="noopener" target="_blank">https://twitter.com/arxiv_org/status/1311354903680360449?s=09</a> | other | 1 |
AGI_and_RL | [
93
] | 1 | 2020-10-15T21:18:17 | 2020-10-15T21:18:17 | <a href="https://github.com/openai/neural-mmo" rel="noopener" target="_blank">https://github.com/openai/neural-mmo</a> | other | 1 |
AGI_and_RL | [
94
] | 1 | 2020-10-19T02:10:32 | 2020-10-19T02:10:32 | <a href="https://github.com/LantaoYu/MARL-Papers" rel="noopener" target="_blank">https://github.com/LantaoYu/MARL-Papers</a> | other | 1 |
AGI_and_RL | [
95
] | 1 | 2020-10-22T05:33:21 | 2020-10-22T05:33:21 | <a href="https://openreview.net/attachment?id=Syx7A3NFvH&name=original_pdf" rel="noopener" target="_blank">https://openreview.net/attachment?id=Syx7A3NFvH&name=original_pdf</a> | other | 1 |
AGI_and_RL | [
96
] | 1 | 2020-10-23T03:13:10 | 2020-10-23T03:13:10 | Open Multi-Agent Systems with Variable Size: the Case of Gossiping <a href="https://arxiv.org/abs/2009.02970" rel="noopener" target="_blank">https://arxiv.org/abs/2009.02970</a> | other | 1 |
AGI_and_RL | [
97,
98,
99
] | 3 | 2020-10-25T10:03:01 | 2020-10-25T10:26:40 | Stability of Open Multi-Agent Systems and Applications to Dynamic Consensus <a href="https://arxiv.org/abs/1906.00890" rel="noopener" target="_blank">https://arxiv.org/abs/1906.00890</a>
<b>Hierarchical Control of Multi-Agent Systems using Online Reinforcement Learning</b> <a href="https://arxiv.org/abs/2007.14186" re... | other | 1 |
AGI_and_RL | [
100
] | 1 | 2020-10-26T05:54:29 | 2020-10-26T05:54:29 | RANDOMIZED ENTITY-WISE FACTORIZATION FOR<br/>MULTI-AGENT REINFORCEMENT LEARNING <a href="https://arxiv.org/pdf/2006.04222.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/2006.04222.pdf</a> | other | 1 |
AGI_and_RL | [
102
] | 1 | 2020-10-27T22:54:18 | 2020-10-27T22:54:18 | Multi-agent RL: AI-QMIX: Attention and Imagination for<br/>Dynamic Multi-Agent Reinforcement Learning <a href="https://arxiv.org/pdf/2006.04222.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/2006.04222.pdf</a> | other | 1 |
AGI_and_RL | [
103
] | 1 | 2020-10-28T17:55:28 | 2020-10-28T17:55:28 | Scaling Up Multiagent Reinforcement Learning for Robotic Systems:<br/>Learn an Adaptive Sparse Communication Graph <a href="https://arxiv.org/pdf/2003.01040.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/2003.01040.pdf</a> | other | 1 |
AGI_and_RL | [
104
] | 1 | 2020-10-29T22:40:52 | 2020-10-29T22:40:52 | A Unified Game-Theoretic Approach to<br/>Multiagent Reinforcement Learning <a href="https://arxiv.org/pdf/1711.00832.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/1711.00832.pdf</a> | other | 1 |
AGI_and_RL | [
105
] | 1 | 2020-10-30T20:21:11 | 2020-10-30T20:21:11 | Projective simulation for artificial intelligence <a href="https://arxiv.org/pdf/1104.3787.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/1104.3787.pdf</a> | other | 1 |
AGI_and_RL | [
106
] | 1 | 2020-10-30T20:56:13 | 2020-10-30T20:56:13 | NCSRR OpenSim <a href="https://opensim.stanford.edu/about/index.html" rel="noopener" target="_blank">https://opensim.stanford.edu/about/index.html</a> | other | 1 |
AGI_and_RL | [
107
] | 1 | 2020-10-31T13:29:03 | 2020-10-31T13:29:03 | <a href="https://github.com/stefan-jansen/machine-learning-for-trading" rel="noopener" target="_blank">https://github.com/stefan-jansen/machine-learning-for-trading</a> | other | 1 |
AGI_and_RL | [
108
] | 1 | 2020-10-31T20:05:11 | 2020-10-31T20:05:11 | Detailed Rigid Body Simulation<br/>with Extended Position Based Dynamics <a href="https://matthias-research.github.io/pages/publications/PBDBodies.pdf" rel="noopener" target="_blank">https://matthias-research.github.io/pages/publications/PBDBodies.pdf</a> | other | 1 |
AGI_and_RL | [
109
] | 1 | 2020-11-01T20:37:26 | 2020-11-01T20:37:26 | Multi-agent Social Reinforcement Learning Improves Generalization <a href="https://arxiv.org/pdf/2010.00581.pdf" rel="noopener" target="_blank">https://arxiv.org/pdf/2010.00581.pdf</a> | other | 1 |
AGI_and_RL | [
110,
111,
112,
113,
114
] | 5 | 2020-11-02T04:20:08 | 2020-11-02T04:53:58 | MARLeME: A Multi-Agent Reinforcement Learning Model Extraction Library <a href="https://arxiv.org/abs/2004.07928" rel="noopener" target="_blank">https://arxiv.org/abs/2004.07928</a>
<a href="https://z0ngqing.github.io/project/learning/" rel="noopener" target="_blank">https://z0ngqing.github.io/project/learning/</a>
C... | other | 1 |
AGI_and_RL | [
115,
116
] | 2 | 2020-11-02T17:29:25 | 2020-11-02T17:32:09 | COLLABORATIVE MULTIAGENT REINFORCEMENT<br/>LEARNING IN HOMOGENEOUS SWARMS <a href="https://openreview.net/pdf?id=ByeDojRcYQ" rel="noopener" target="_blank">https://openreview.net/pdf?id=ByeDojRcYQ</a>
Improving coordination in small-scale multi-agent deep reinforcement learning through memory-driven communication <a h... | other | 1 |
AGI_and_RL | [
117,
118,
119,
120
] | 4 | 2020-11-02T21:41:07 | 2020-11-02T21:53:15 | Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning<br/> <a href="https://arxiv.org/abs/2006.10800" rel="noopener" target="_blank">https://arxiv.org/abs/2006.10800</a>
QTran: Learning to Factorize with Transformation <a href="http://www.kasimte.com/2019/12/12/wha... | other | 1 |
AGI_and_RL | [
121
] | 1 | 2020-11-03T01:53:49 | 2020-11-03T01:53:49 | From Few to More: Large-scale Dynamic Multiagent Curriculum Learning <a href="https://arxiv.org/abs/1909.02790" rel="noopener" target="_blank">https://arxiv.org/abs/1909.02790</a> | other | 1 |
AGI_and_RL | [
123,
124
] | 2 | 2020-11-07T06:00:33 | 2020-11-07T06:00:42 | <a href="https://medium.com/pytorch/robotic-assembly-using-deep-reinforcement-learning-dfd9916c5ad7" rel="noopener" target="_blank">https://medium.com/pytorch/robotic-assembly-using-deep-reinforcement-learning-dfd9916c5ad7</a>
<a href="https://ieeexplore.ieee.org/document/8594353" rel="noopener" target="_blank">https:... | other | 1 |
AGI_and_RL | [
126
] | 1 | 2020-11-07T13:23:01 | 2020-11-07T13:23:01 | HILONet: Hierarchical Imitation Learning from Non-Aligned Observations <a href="https://arxiv.org/abs/2011.02671" rel="noopener" target="_blank">https://arxiv.org/abs/2011.02671</a> | other | 1 |
AGI_and_RL | [
127,
128
] | 2 | 2020-11-08T04:59:55 | 2020-11-08T05:00:10 | Reinforcement Learning with Human Teachers: Evidence of Feedback and<br/>Guidance with Implications for Learning Performance <a href="http://robotic.media.mit.edu/wp-content/uploads/sites/7/2015/01/Thomaz-etal-AAAI-06.pdf" rel="noopener" target="_blank">http://robotic.media.mit.edu/wp-content/uploads/sites/7/2015/01/Th... | other | 1 |
AGI_and_RL | [
129
] | 1 | 2020-11-08T07:17:31 | 2020-11-08T07:17:31 | <a href="https://github.com/arrival-ltd/catalyst-rl-tutorial" rel="noopener" target="_blank">https://github.com/arrival-ltd/catalyst-rl-tutorial</a> | other | 1 |
AGI_and_RL | [
130
] | 1 | 2020-11-10T16:00:58 | 2020-11-10T16:00:58 | <a href="https://www.nature.com/articles/s41467-019-13073-w" rel="noopener" target="_blank">https://www.nature.com/articles/s41467-019-13073-w</a> | other | 1 |
AGI_and_RL | [
131,
132,
133,
134,
135,
136
] | 6 | 2020-11-12T17:36:06 | 2020-11-12T17:47:06 | QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning <a href="https://arxiv.org/abs/1803.11485" rel="noopener" target="_blank">https://arxiv.org/abs/1803.11485</a>.
Multi-Agent Collaboration via Reward Attribution Decomposition <a href="https://arxiv.org/abs/2010.08531" rel="noopene... | other | 1 |
AGI_and_RL | [
137
] | 1 | 2020-11-13T10:59:47 | 2020-11-13T10:59:47 | <a href="https://github.com/google-research/recsim" rel="noopener" target="_blank">https://github.com/google-research/recsim</a> | other | 1 |
AGI_and_RL | [
138
] | 1 | 2020-11-15T14:52:41 | 2020-11-15T14:52:41 | <b>Reinforcement Learning with Videos: Combining Offline Observations with Interaction</b> <a href="https://arxiv.org/abs/2011.06507" rel="noopener" target="_blank">https://arxiv.org/abs/2011.06507</a> | other | 1 |
AGI_and_RL | [
139
] | 1 | 2020-11-15T18:00:02 | 2020-11-15T18:00:02 | <b>Continual Learning of Control Primitives: Skill Discovery via Reset-Games</b> <a href="https://arxiv.org/abs/2011.05286" rel="noopener" target="_blank">https://arxiv.org/abs/2011.05286</a> | other | 1 |
AGI_and_RL | [
140
] | 1 | 2020-11-16T09:14:34 | 2020-11-16T09:14:34 | <b>Learning Latent Representations to Influence Multi-Agent Interaction</b> <a href="https://arxiv.org/abs/2011.06619" rel="noopener" target="_blank">https://arxiv.org/abs/2011.06619</a> | other | 1 |
End of preview. Expand in Data Studio
No dataset card yet
- Downloads last month
- 90