MATD3-Walker
Multi-Agent TD3 Cooperative Learning
Walker Environment
@article{terry2020pettingzoo, Title = {PettingZoo: Gym for Multi-Agent Reinforcement Learning}, Author = {Terry, Justin K and Black, Benjamin and Jayakumar, Mario and Hari, Ananth and Santos, Luis and Dieffendahl, Clemens and Williams, Niall L and Lokesh, Yashas and Sullivan, Ryan and Horsch, Caroline and Ravi, Praveen}, journal={arXiv preprint arXiv:2009.14471}, year={2020} }
SISL Environments
n_walkers: number of bipedal walker agents in environment
position_noise: noise applied to agent positional sensor observations
angle_noise: noise applied to agent rotational sensor observations
local_ratio: Proportion of reward allocated locally vs distributed among all agents
forward_reward: reward applied for an agent standing, scaled by agent’s x coordinate
fall_reward: reward applied when an agent falls down
terminate_reward: reward applied to a walker for failing the environment
terminate_on_fall: toggles whether agent is done if it falls down
remove_on_fall: Remove walker when it falls (only does anything when terminate_on_fall is False)
max_cycles: after max_cycles steps all agents will return done
@inproceedings{gupta2017cooperative, title={Cooperative multi-agent control using deep reinforcement learning}, author={Gupta, Jayesh K and Egorov, Maxim and Kochenderfer, Mykel}, booktitle={International Conference on Autonomous Agents and Multiagent Systems}, pages={66--83}, year={2017}, organization={Springer} }
Experience Replay
Simple experience reply and priority experience replay.
@misc{baselines, author = {Dhariwal, Prafulla and Hesse, Christopher and Klimov, Oleg and Nichol, Alex and Plappert, Matthias and Radford, Alec and Schulman, John and Sidor, Szymon and Wu, Yuhuai and Zhokhov, Peter}, title = {OpenAI Baselines}, year = {2017}, publisher = {GitHub}, journal = {GitHub repository}, howpublished = {\url{https://github.com/openai/baselines}}, }