This repository contains source code for Pommerman experiments in the paper titled "Learning to Play Imperfect-Information Games by Imitating an Oracle Planner" by Rinu Boney, Alexander Ilin, Juho Kannala and Jarno Seppänen.
Dependencies:
- pommerman package from https://github.com/MultiAgentLearning/playground
- cpommerman (see
cython_envfor installation instructions) - numpy 1.18.1
- pytorch 1.5.0
The planning results reported in the paper can be reproduced by running:
python plan.py
Optional arguments:
| Parameter | Default | Description |
|---|---|---|
| --planner | fdts | 'fdts' or 'mcts' or 'mcs' |
| --mab | ts | 'ts' or 'ucb' |
| --n_simulations | 100 | number of planning rollouts at each time-step |
| --horizon | 20 | depth of planning rollouts in FDTS and MCS |
| --n_threads | 5 | number of games to play in parallel |
| --n_episodes | 100 | total number of games to play |
The follower results reported in the paper can be reproduced by running:
python follow.py
This project is licensed under the terms of the GPL-3.0 License. See LICENSE file for details.