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Multiagent Reinforcement Learning: Rollout and Policy Iteration

Implementation of the Multiagent Rollout based on the paper by Dimitri Bertsekas (2021).

Environment

Simulation environment follows the rules of the Spiders-and-Flies game as specified in [1]. The environment is adapted from Anurag Koul's ma-gym [2] modifying the PredatorPrey env.

Usage

  • Install the requirements with pip:
$ pip install -r requirements.txt
  • Run the agent simulation from the scripts folder:
$ python run_agent.py
  • Run agents' comparison from the scripts folder:
$ python run_comparison.py

Results

Baseline Policy Standard Rollout Agent-by-agent Rollout

Note: Baseline Policy means moving along the shortest path to the closest surviving fly.

References

  1. Dimitri Bertsekas - Multiagent Reinforcement Learning: Rollout and Policy Iteration (2021). Web: https://ieeexplore.ieee.org/document/9317713

  2. Anurag Koul - ma-gym: Collection of multi-agent environments based on OpenAI gym (2019). Web: https://github.com/koulanurag/ma-gym