MARL Environment - Multi Agent Reinforcement Learning Environment
BomberManAI is an implementation of a simplified version of the classic game. Here walls are not used and there are no special events/powers for players. Your goal is to collect as many coins as you can. At the same time, you have to avoid bombs that can harm the player by taking away coins. You can use bombs to hurt your opponent and decrease his score. The limit of game is number of episodes or score limit. The winner is the player who has collected the most coins.
You can run the environment in multiple configurations. An agent learned using RL can face an agent learned using evolutionary computation or your own written heuristics. You can also control the agent yourself, trying your hand against learned opponents. Examples of how to train agents can be found here - BomberManAI/bomberman/training.
python == 3.8
pettingzoo == 1.16.0
gym == 0.21.0
pygame == 2.1.0
ray == 1.10.0
torch == 1.11.0
deap == 1.3.1
./game/config.py
In above file you can find all game settings. You can manipulate them to adjust the environment.
from bomberman.usage.manual_policy import run_manual_policy
run_manual_policy()
from bomberman.usage.manual_control import run_manual_control
run_manual_control()
from bomberman.usage.random_simulation import run_random_simulation
run_random_simulation()