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Hello, I pushed some python environments for Multi Agent Reinforcement Learning.
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# 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.
<font color=Blue>action_list</font> 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.
<font color=Blue>obs_list</font> 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}
}
```
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matched fp:97f567df424ea84d, name:multi-agent, desc:multi-agent, name:multi agent
matched fp:97f567df424ea84d, name:reinforcement learning, desc:reinforcement learning