This repo implements the state-of-the-art methods for deep RL in a networked multi-agent system, with observability and communication of each agent limited to its neighborhood. For fair comparison, all methods are applied to A2C agents. Under construction ...
Available IA2C algorithms:
- PolicyInferring: Lowe, Ryan, et al. "Multi-agent actor-critic for mixed cooperative-competitive environments." Advances in Neural Information Processing Systems, 2017.
- FingerPrint: Foerster, Jakob, et al. "Stabilising experience replay for deep multi-agent reinforcement learning." arXiv preprint arXiv:1702.08887, 2017.
- ConsensusUpdate: Zhang, Kaiqing, et al. "Fully decentralized multi-agent reinforcement learning with networked agents." arXiv preprint arXiv:1802.08757, 2018.
Available MA2C algorithms:
- DIAL: Foerster, Jakob, et al. "Learning to communicate with deep multi-agent reinforcement learning." Advances in Neural Information Processing Systems. 2016.
- CommNet: Sukhbaatar, Sainbayar, et al. "Learning multiagent communication with backpropagation." Advances in Neural Information Processing Systems, 2016.
- NeurComm: Gilmer, Justin, et al. "Neural message passing for quantum chemistry." arXiv preprint arXiv:1704.01212, 2017.
- Python3
- Tensorflow
- SUMO