| openai/multi-agent-emergence-environments |
1,426 |
|
0 |
0 |
about 3 years ago |
0 |
|
26 |
mit |
Python |
| Environment generation code for the paper "Emergent Tool Use From Multi-Agent Autocurricula" |
| openai/evolution-strategies-starter |
1,253 |
|
0 |
0 |
over 6 years ago |
0 |
|
14 |
mit |
Python |
| Code for the paper "Evolution Strategies as a Scalable Alternative to Reinforcement Learning" |
| openai/multiagent-competition |
614 |
|
0 |
0 |
over 6 years ago |
0 |
|
12 |
|
Python |
| Code for the paper "Emergent Complexity via Multi-agent Competition" |
| RITCHIEHuang/DeepRL_Algorithms |
112 |
|
0 |
0 |
almost 5 years ago |
0 |
|
1 |
gpl-3.0 |
Python |
| DeepRL algorithms implementation easy for understanding and reading with Pytorch and Tensorflow 2(DQN, REINFORCE, VPG, A2C, TRPO, PPO, DDPG, TD3, SAC) |
| spitis/mrl |
95 |
|
0 |
0 |
almost 3 years ago |
0 |
|
8 |
mit |
Python |
| implementation-matters/code-for-paper |
59 |
|
0 |
0 |
over 6 years ago |
0 |
|
5 |
|
Python |
| aravindr93/hand_dapg |
49 |
|
0 |
0 |
over 6 years ago |
0 |
|
3 |
apache-2.0 |
Python |
| Repository to accompany RSS 2018 paper on dexterous hand manipulation |
| nicklashansen/policy-adaptation-during-deployment |
46 |
|
0 |
0 |
over 5 years ago |
0 |
|
0 |
|
Python |
| Training code and evaluation benchmarks for the "Self-Supervised Policy Adaptation during Deployment" paper. |
| twni2016/f-IRL |
33 |
|
0 |
0 |
over 2 years ago |
0 |
|
1 |
mit |
Python |
| Inverse Reinforcement Learning via State Marginal Matching - CoRL 2020 |
| yilundu/ebm_compositionality |
31 |
|
0 |
0 |
about 4 years ago |
0 |
|
3 |
|
Python |
| [NeurIPS'20] Code for the Paper Compositional Visual Generation and Inference with Energy Based Models |
