YOLO-MARL: You Only LLM Once for Multi-Agent Reinforcement Learning

This is the official repository for the paper "YOLO-MARL: You Only LLM Once for Multi-agent Reinforcement Learning".

YOLO-MARL supports both ChatGPT and Claude. Please put your api KEY in the YOLO-MARL/ and name it claude_KEY.txt or openai_KEY.txt.

In YOLO-MARL/src/config/default.yaml, you need to set use_llm by yourself. False for running the MARL baselines and True for running the YOLO-MARL method.

All the baselines we used here are based on Epymarl. For the denpendencies, you could also refer to Epymarl and then install Openai or Claude dependencies:

pip install openai #If you want to use ChatGPT for your LLM API
pip install anthropic #If you want to use Claude for your LLM API

Baseline Experiments

For our baselines experiments, you could find all the hyperparameters in "Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks" for the Level-Based Foraging (LBF) and Multi-Robot Warehouse (RWARE) environments, and MPE.

For the YOLO-MARL method, please set the environment in LLM-copilot-RL/LBF/src/prompts/config/config.yaml before you generate LLM planning function.

How to use YOLO-MARL to generate planning function for testing environment:

python YOLO-MARL/src/prompts/codeGeneration.py

For YOLO-MARL training, please set the hyperparameters llm_reward and penalty in YOLO-MARL/src/envs/llm_wrapper.py for mixed_constant.

LBF:

python src/main.py --config=qmix --env-config=gymma with env_args.time_limit=50 env_args.key="lbforaging:Foraging-8x8-2p-3f-v3"

You can run experiments in these environments as follows:

LBF:

python src/main.py --config=qmix --env-config=gymma with env_args.time_limit=50 env_args.key="lbforaging:Foraging-8x8-2p-3f-v3"

RWARE:

python src/main.py --config=mappo --env-config=gymma with env_args.time_limit=500 env_args.key="rware:rware-tiny-2ag-v2"

MPE:

python src/main.py --config=qmix --env-config=gymma with env_args.time_limit=25 env_args.key="pz-mpe-simple-spread-v3"

Note that for the MPE environments tag (predator-prey) and adversary, we provide pre-trained prey and adversary policies. These can be used to control the respective agents to make these tasks fully cooperative (used in the paper) by setting env_args.pretrained_wrapper="PretrainedTag" or env_args.pretrained_wrapper="PretrainedAdversary".

SMAC:

python src/main.py --config=qmix --env-config=sc2 with env_args.map_name="3s5z"

Below, we provide the base environment and key / map name for all the environments evaluated in the "Benchmarking Multi-Agent Deep Reinforcement Learning Algorithms in Cooperative Tasks":

• LBF: all with --env-config=gymma with env_args.time_limit=50 env_args.key="..."
  • 8x8-2p-2f-coop: lbforaging:Foraging-8x8-2p-2f-coop-v3
  • 8x8-2p-2f-2s-coop: lbforaging:Foraging-2s-8x8-2p-2f-coop-v3
  • 10x10-3p-3f: lbforaging:Foraging-10x10-3p-3f-v3
  • 10x10-3p-3f-2s: lbforaging:Foraging-2s-10x10-3p-3f-v3
  • 15x15-3p-5f: lbforaging:Foraging-15x15-3p-5f-v3
  • 15x15-4p-3f: lbforaging:Foraging-15x15-4p-3f-v3
  • 15x15-4p-5f: lbforaging:Foraging-15x15-4p-5f-v3
• RWARE: all with --env-config=gymma with env_args.time_limit=500 env_args.key="..."
  • tiny 2p: rware:rware-tiny-2ag-v2
  • tiny 4p: rware:rware-tiny-4ag-v2
  • small 4p: rware:rware-small-4ag-v2
• MPE: all with --env-config=gymma with env_args.time_limit=25 env_args.key="..."
  • simple speaker listener: pz-mpe-simple-speaker-listener-v4
  • simple spread: pz-mpe-simple-spread-v3
  • simple adversary: pz-mpe-simple-adversary-v3 with additional env_args.pretrained_wrapper="PretrainedAdversary"
  • simple tag: pz-mpe-simple-tag-v3 with additional env_args.pretrained_wrapper="PretrainedTag"

Citing YOLO-MARL

We are still working on SMAC and hope we will update the SMAC part soon.