Python module made for training and testing reinforcement learning models. Comes with 2 pre-built models and environments. Currently adding support for enviroments built on top of ROMs.
Since this module is still in development, here is a small sample that currently works.
from coliseum.models import ql
from coliseum.envs import spot
from coliseum.train import memory_replay_threaded
env = spot.Spot()
model = ql.QL(env=env)
memory_replay_threaded(model, env)All models have been verified to work on the spot environment.
Q-Learning algorithm. Read more about it here. Simple model-free reinforcement learning algorithm. As the name suggests, it's core formula is the base for the deep q-learning methods.
Simple Deep Q-Learning Network. Read more about the general structure here. Consider this the baseline/bare minimum in terms of modern deep reinforcement models.
Spot is a game where the player just has to select the cell that contains a one. This enviornment should be used just for debugging.
Classic Version of TicTacToe against a random opponent. Players which attempt to make an illegal move are penalized and lose their turn.
- Better Documentation
- Setup CI/tests
- Self play
- Add more models
- Add more environments