README.md

MAILSwiss

This is a multi-agent version of ILSwiss, which contains various reinforcement learning and imitation learning algorithms. Now the multi-agent interface only supports independent training among agents, and more complicated interacting interface such as CTDE (Centralized Training with Decentralized Execution) will be implemented in the future. Any contributions are welcome!

Current Available Algorithms

• Soft Actor Critic

• Proximal Policy Optimization

• Generative Adversarial Imitation Learning (GAIL)

• Behavior Cloning (BC)

• QMIX

• MADDPG

Setup

1. Configurate conda environment:

   conda env create --name mail --file=env.yml
   pip install -e .

2. Run RL algorithms to obtain expert policy:

   python run_experiments.py -e exp_specs/sac/sac_mpe_spread.yaml

3. Generate expert demostrations using trained policy:

   First change policy_log_dir in exp_specs/gen_expert/mpe_spread.yaml to the path of rl training log directory, then run:

   python run_experiments.py -e exp_specs/gen_expert/mpe_spread.yaml

   Add the path of generated demonstrations in demos_listing.yaml.

4. Run GAIL:

   Make sure expert_name in exp_specs/gail/gail_mpe_spread.yaml is the same as the name in demos_listing.yaml, then run:

   python run_experiments.py -e exp_specs/gail/gail_mpe_spread.yaml

5. Run BC:

   Make sure expert_name in exp_specs/bc/bc_mpe_spread.yaml is the same as the name in demos_listing.yaml, then run:

   python run_experiments.py -e exp_specs/bc/bc_mpe_spread.yaml