jk96491/c-coma

Continual Multi-agent Reinforcement Learning in Dynamic Environments

C-COMA

논문번호 : KIPS_C2020B0262

• C-COMA: A Continual Reinforcement Learning Model for Dynamic Multiagent Environments

Architecture

Dynamic environment Image

StarCraft II Multi Agent Challenge

This repository is a guide edited for convenient execution in the Windows OS.

First you need to install the StarCraft 2 game. Trial version does not matter. Download it from the link below

https://starcraft2.com/ko-kr/

After installation, you should download the map required for the minigame from the link below.

https://github.com/oxwhirl/smac/tree/master/smac/env/starcraft2/maps/SMAC_Maps

You can move all downloaded files to the path below.

"C:\Program Files (x86)\StarCraft II\Maps\SMAC_Maps"

The dynamic environment used in this paper is inside DynamicMaps. You can put these files in the upper path.

Training Env : Dynamic_env_Training.SC2Map

Testing Env : Dynamic_env_Test1.SC2Map

Installation instructions

Build the Dockerfile using

cd docker
bash build.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.

The requirements.txt file can be used to install the necessary packages into a virtual environment (not recomended).

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.

Documentation/Support

Documentation is a little sparse at the moment (but will improve!). Please raise an issue in this repo, or email Tabish

Citing PyMARL

If you use PyMARL in your research, please cite the SMAC paper.

M. Samvelyan, T. Rashid, C. Schroeder de Witt, G. Farquhar, N. Nardelli, T.G.J. Rudner, C.-M. Hung, P.H.S. Torr, J. Foerster, S. Whiteson. The StarCraft Multi-Agent Challenge, CoRR abs/1902.04043, 2019.

In BibTeX format:

@article{samvelyan19smac,
  title = {{The} {StarCraft} {Multi}-{Agent} {Challenge}},
  author = {Mikayel Samvelyan and Tabish Rashid and Christian Schroeder de Witt and Gregory Farquhar and Nantas Nardelli and Tim G. J. Rudner and Chia-Man Hung and Philiph H. S. Torr and Jakob Foerster and Shimon Whiteson},
  journal = {CoRR},
  volume = {abs/1902.04043},
  year = {2019},
}

License

Code licensed under the Apache License v2.0

c-coma's Issues

Dynamic environment

Hi! Your article is great of original and thanks for your code.How does continuous learning solve dynamic environment problems in code? Can you elaborate? thank you

Paper in English

Is there a english release of your paper?