/mario-rl-tutorial

Reinforcement Learning Tutorial on Super Mario

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mario-rl-tutorial

Gym Super Mario is an environment bundle for OpenAI Gym

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