MAG

This code accompanies the paper "Models as Agents: Optimizing Multi-Step Predictions of Interactive Local Models in Model-Based Multi-Agent Reinforcement Learning".

The repository contains MAG implementation as well as fine-tuned hyperparameters in configs/dreamer/optimal folder.

Usage

python3 train.py --n_workers 2 --starcraft

Optimal parameters

The optimal parameters are contained in configs/dreamer/optimal/ folder.

SMAC

The code for the environment can be found at https://github.com/oxwhirl/smac

Code Structure

• agent contains implementation of MAG
  • controllers contains logic for inference
  • learners contains logic for learning the agent
  • memory contains buffer implementation
  • models contains architecture of MAG
  • optim contains logic for optimizing loss functions
  • runners contains logic for running multiple workers
  • utils contains helper functions
  • workers contains logic for interacting with environment
• env contains environment logic
• networks contains neural network architectures