Multi-Agent-Learning-Environments
Hello, I pushed some python environments for Multi Agent Reinforcement Learning. Some are single agent version that can be used for algorithm testing. I provide documents for each environment, you can check the corresponding pdf files in each directory. These are just toy problems, though some of them are still hard to solve. Some environments are like:
Multi Agent Soccer Game
Multi Agent Rescue
Multi Agent Cleaner
Multi Agent Move Box
Multi Agent Catching Pig
Multi Drones Monitoring
Multi Agent Maze Running
Multi Agent Find Treasure
Firefighters
Go Together
Warehouse
Opposite
Dependency
OpenCV, swig
Multi-Agent Environment Standard
Assumption:
Each agent works synchronously.
Member Functions
reset()
reward_list, done = step(action_list)
obs_list = get_obs()
reward_list records the single step reward for each agent, it should be a list like [reward1, reward2,......]. The length should be the same as the number of agents. Each element in the list should be a integer.
done True/False, mark when an episode finishes.
action_list records the single step action instruction for each agent, it should be a list like [action1, action2,...]. The length should be the same as the number of agents. Each element in the list should be a non-negative integer.
obs_list records the single step observation for each agent, it should be a list like [obs1, obs2,...]. The length should be the same as the number of agents. Each element in the list can be any form of data, but should be in same dimension, usually a list of variables or an image.
Typical Monte Carlo Procedures
reset environment by calling reset() get initial observation get_obs() for i in range(max_MC_iter): get action_list from controller apply action by step() record returned reward list record new observation by get_obs()
Citation
Cite the environment of the following paper as:
@inproceedings{jiang2021multi,
title={Multi-agent reinforcement learning with directed exploration and selective memory reuse},
author={Jiang, Shuo and Amato, Christopher},
booktitle={Proceedings of the 36th Annual ACM Symposium on Applied Computing},
pages={777--784},
year={2021}
}