wanghuimu/pymarl2

Rethinking the Importance of Implementation Tricks in Multi-Agent Reinforcement Learning

MARL Tricks

Our codes for RIIT: Rethinking the Importance of Implementation Tricks in Multi-AgentReinforcement Learning. We implemented and standardized the hyperparameters of the SOTA MARL algorithms.

Python MARL framework

PyMARL is WhiRL's framework for deep multi-agent reinforcement learning and includes implementations of the following algorithms:

Value-based Methods:

• QMIX: QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
• VDN: Value-Decomposition Networks For Cooperative Multi-Agent Learning
• IQL: Independent Q-Learning
• QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning
• MAVEN: MAVEN: Multi-Agent Variational Exploration
• Qatten: Qatten: A general framework for cooperative multiagent reinforcement learning
• QPLEX: Qplex: Duplex dueling multi-agent q-learning
• WQMIX: Weighted QMIX: Expanding Monotonic Value Function Factorisation

Actor Critic Methods:

• COMA: Counterfactual Multi-Agent Policy Gradients
• VMIX: Value-Decomposition Multi-Agent Actor-Critics
• FacMADDPG: Deep Multi-Agent Reinforcement Learning for Decentralized Continuous Cooperative Control
• LICA: Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning
• DOP: Off-Policy Multi-Agent Decomposed Policy Gradients
• RIIT: RIIT: Rethinking the Importance of Implementation Tricks in Multi-AgentReinforcement Learning

PyMARL is written in PyTorch and uses SMAC as its environment.

Installation instructions

Install Python packages

# require Anaconda 3 or Miniconda 3
bash install_dependecies.sh

Set up StarCraft II and SMAC:

bash install_sc2.sh

This will download SC2 into the 3rdparty folder and copy the maps necessary to run over.

Run an experiment

# For SMAC
python3 src/main.py --config=qmix --env-config=sc2 with env_args.map_name=corridor

# For Cooperative Predator-Prey
python3 src/main.py --config=qmix_prey --env-config=stag_hunt with env_args.map_name=stag_hunt

The config files act as defaults for an algorithm or environment.

They are all located in src/config. --config refers to the config files in src/config/algs --env-config refers to the config files in src/config/envs

Run parallel experiments:

# bash run.sh config_name map_name_list (threads_num arg_list gpu_list experinments_num)
bash run.sh qmix corridor 2 epsilon_anneal_time=500000 0,1 5

xxx_list is separated by ,.

All results will be stored in the Results folder and named with map_name.

Force all processes to exit

# all python and game processes of current user will quit.
bash clean.sh

Some test results on Super Hard scenarios

Cite

@article{hu2021riit,
      title={RIIT: Rethinking the Importance of Implementation Tricks in Multi-Agent Reinforcement Learning}, 
      author={Jian Hu and Haibin Wu and Seth Austin Harding and Siyang Jiang and Shih-wei Liao},
      year={2021},
      eprint={2102.03479},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}