PROMOTING COORDINATION VIA ATTENTION-BASED POLICY REGULARIZATION IN MULTI-AGENT TEAMS

Implementation of the method (6.867 Project)

Installation instructions

Install Python packages

# require Anaconda 3 or Miniconda 3
bash install_dependecies.sh

Set up StarCraft II (2.4.10) and SMAC:

bash install_sc2.sh

This will download SC2.4.10 into the 3rdparty folder and copy the maps necessary to run over.

Set up Google Football:

bash install_gfootball.sh

Command Line Tool

Run an experiment

# For SMAC (Our method)
python3 src/main.py --config=alita --env-config=sc2 with env_args.map_name=corridor

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

# For Difficulty-Enhanced Predator-Prey
python3 src/main.py --config=qmix_predator_prey --env-config=stag_hunt with env_args.map_name=stag_hunt

# For Communication tasks
python3 src/main.py --config=qmix_att --env-config=sc2 with env_args.map_name=1o_10b_vs_1r

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 n parallel experiments

# bash run.sh config_name env_config_name map_name_list (arg_list threads_num gpu_list experinments_num)
bash run.sh qmix sc2 6h_vs_8z epsilon_anneal_time=500000,td_lambda=0.3 2 0 5

xxx_list is separated by ,.

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

Kill all training processes

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