| oxwhirl/pymarl |
1,304 |
|
0 |
0 |
over 3 years ago |
0 |
|
50 |
apache-2.0 |
Python |
| Python Multi-Agent Reinforcement Learning framework |
| clvrai/awesome-rl-envs |
835 |
|
0 |
0 |
over 2 years ago |
0 |
|
3 |
|
|
| datamllab/awesome-game-ai |
522 |
|
0 |
0 |
about 3 years ago |
0 |
|
0 |
mit |
|
| Awesome Game AI materials of Multi-Agent Reinforcement Learning |
| TonghanWang/ROMA |
106 |
|
0 |
0 |
over 4 years ago |
0 |
|
9 |
apache-2.0 |
Python |
| Codes accompanying the paper "ROMA: Multi-Agent Reinforcement Learning with Emergent Roles" (ICML 2020 https://arxiv.org/abs/2003.08039) |
| simonmeister/pysc2-rl-agents |
99 |
|
0 |
0 |
over 7 years ago |
0 |
|
4 |
mit |
Python |
| StarCraft II / PySC2 Deep Reinforcement Learning Agents (A2C) |
| DRL-CASIA/StarCraft-AI |
95 |
|
0 |
0 |
over 8 years ago |
0 |
|
0 |
|
C++ |
| Reinforcement Learning and Transfer Learning based StarCraft Micromanagement |
| TeamSAIDA/SAIDA_RL |
53 |
|
0 |
0 |
about 6 years ago |
0 |
|
5 |
mit |
C++ |
| jk96491/SMAC |
29 |
|
0 |
0 |
over 4 years ago |
0 |
|
3 |
apache-2.0 |
Python |
| StarCraft II Multi Agent Challenge : QMIX, COMA, LIIR, QTRAN, Central V, ROMA, RODE, DOP, Graph MIX |
| roop-pal/Meta-Learning-for-StarCraft-II-Minigames |
20 |
|
0 |
0 |
about 5 years ago |
0 |
|
0 |
|
Python |
| We reproduced DeepMind's results and implement a meta-learning (MLSH) agent which can generalize across minigames. |
| jingranburangyongzhongwen/torchMARL |
15 |
|
0 |
0 |
almost 5 years ago |
0 |
|
0 |
|
Python |
| pytorch实现的一些MARL算法 |
