CAMA

Code for ICML2023 accepted paper: Complementary Attention for Multi-Agent Reinforcement Learning.

This codebase is built on top of the PyMARL framework and the codebase of REFIL algorithm. Thanks for Shariq Iqbal for sharing his code.

Setup instructions

Please follow the instructions in REFIL codebase. Note: If you want to run environment sc2custom, an empty map needs to be copied to the SC2 directory. Note: The particle environment uses gym==0.10.5.

Run an experiment

Run an ALGORITHM from the folder src/config/algs in an ENVIRONMENT from the folder src/config/envs

export CUDA_VISIBLE_DEVICES="0" && python src/main.py --env-config=<ENVIRONMENT> --config=<ALGORITHM> with <PARAMETERS>

Possible environments are:

• particle: Resource collection.
• sc2custom: StarCraft.
• traffic_junction: Traffic Junction.
• gridworld: The demo "Catch Apple" in the paper.

Command examples

Run CAMA with Resource collection:

export CUDA_VISIBLE_DEVICES="0" && python CAMA/main.py --env-config=particle --config=cama_qmix_atten with test_unseen=False

Run CAMA with sc2:

export CUDA_VISIBLE_DEVICES="0" && python CAMA/main.py --env-config=sc2custom --config=cama_refil with test_unseen=False scenario=3-8sz_symmetric

Parameter Setting

The parameter names in the article correspond to the parameter settings in the code as follows:

• Common:
  • $\alpha$: rank_percent
  • $\beta$: beta
  • weight of $\mathcal{L}_{IM}$: ce_weight
  • weight of $\mathcal{L}_{MI}$: club_weight
• Resource Collection:
  • sight range: env_args.sight_range_kind (a dictionary of {sight_range_kind:true sight range in maps}: {0:0.2, 1:0.5, 2:1.0, 3:0.8, 4:$\infty$, 5:1.5, 6:2.0})
• SC2:
  • sight range: env_args.sight_range
• Traffic Junction:
  • sight range: env_args.vision (0 for 0, 1 for 3*3 grids)