pestyvesty/mario-rl-tutorial

Reinforcement Learning Tutorial on Super Mario

mario-rl-tutorial

Gym Super Mario is an environment bundle for OpenAI Gym

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Installation

pip3 install git+https://github.com/chris-chris/gym-super-mario
pip3 install git+https://github.com/openai/baselines

To load and run the environments, run

import gym
import ppaquette_gym_super_mario
env = gym.make('ppaquette/SuperMarioBros-1-1-v0')

To train the model, run

python3 train.py

Paramters

You can also customize the training with parameters.

python3 train.py --algorithm=deepq --timesteps=2000000 --log=stdout --env=ppaquette/SuperMarioBros-1-1-v0

	Description	Default	Parameter Type
env	Gym Environment	ppaquette/SuperMarioBros-1-1-v0	string
log	logging type : tensorboard, stdout	stdout	string
algorithm	Currently, support 2 algorithms : deepq, acktr	deepq	string
timesteps	Total training steps	2000000	int
exploration_fraction	exploration fraction	0.5	float
prioritized	Whether using prioritized replay for DQN	False	boolean
dueling	Whether using dueling network for DQN	False	boolean
num_cpu	number of agents for A3C(acktr)	4	int