MuDE: Multi-agent Decomposed Reward-based Exploration

Note

This codebase accompanies paper MuDE: Multi-agent decomposed reward-based exploration, and is based on PyMARL and SMAC codebases which are open-sourced.

The implementation of the following methods can also be found in this codebase, which are finished by the authors of following papers:

• QPLEX: QPLEX: Duplex Dueling Multi-Agent Q-Learning
• QMIX: QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning
• RD²: Reward Decomposition with Representation Decomposition

Please refer to PyMARL for setting up the experimental environment.

Run an experiment

SMAC

python3 src/main.py --config=mude --env-config=sc2 with env_args.map_name=3m

Modified Predator-prey (MPP)

python3 src/main.py --config=mude --env-config=pred_prey_punish

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

All results will be stored in the Results folder.

Saving and loading learnt models

Saving models

You can save the learnt models to disk by setting save_model = True, which is set to False by default. The frequency of saving models can be adjusted using save_model_interval configuration. Models will be saved in the result directory, under the folder called models. The directory corresponding each run will contain models saved throughout the experiment, each within a folder corresponding to the number of timesteps passed since starting the learning process.

Loading models

Learnt models can be loaded using the checkpoint_path parameter, after which the learning will proceed from the corresponding timestep.

Watching StarCraft II replays

save_replay option allows saving replays of models which are loaded using checkpoint_path. Once the model is successfully loaded, test_nepisode number of episodes are run on the test mode and a .SC2Replay file is saved in the Replay directory of StarCraft II. Please make sure to use the episode runner if you wish to save a replay, i.e., runner=episode. The name of the saved replay file starts with the given env_args.save_replay_prefix (map_name if empty), followed by the current timestamp.

The saved replays can be watched by double-clicking on them or using the following command:

python -m pysc2.bin.play --norender --rgb_minimap_size 0 --replay NAME.SC2Replay

Note: Replays cannot be watched using the Linux version of StarCraft II. Please use either the Mac or Windows version of the StarCraft II client.