Some tests for algorithms that learn how to play Super Nintendo and other retro games using Deep Reinforcement Learning methods.

First create a Conda environment using one of the provided environment file, either for CPU or GPU computation. GPU is highly recommended.

conda env create -f environment-gpu.yml

After that, activate the environment

source activate gym-retro-gpu

Finally you should install the ROMs for the games you want to try.

The ideal hardware to train the player agent is to make use of a machine with a single GPU and a large number of CPUs. For instance, you could use a g3.4xlarge AWS instance. Be warned that training might take days!

Once you have decided on a game, you should desing a controller mapping for such game. This is to easen the learning process: only allow the agent to perform button combinations that make sense in the game. Just take a look at other examples in the games configuration file.

After this is done you can train on the game for a given level by running

python rllib.py GAME LEVEL --workers CPUS_ON_YOUR_MACHINE

Regular checkpoints will be saved under a folder in your ~/ray-results directory. You can visualize the training metrics by running

tensorboard --logdir=LOGDIR

You can then see the agent playing by running

python rllib.py  GAME LEVEL --test --checkpoint CHECKPOINTFILE

Movies of the playthrough can be recorded by adding the --makemovie argument.

Gradius III (SNES)

python rllib.py GradiusIII-Snes Level1.Mode1.Shield

Donkey Kong Country (SNES)

python rllib.py DonkeyKongCountry-Snes 1Player.CongoJungle.JungleHijinks.Level1

Super Mario World (SNES)

python rllib.py SuperMarioWorld-Snes DonutPlains1

Sonic the Hedgehog (Genesis)

python rllib.py SonicTheHedgehog-Genesis GreenHillZone.Act1

Comix Zone (Genesis)

python rllib.py ComixZone-Genesis Episode1.Page1

Work in progress! But do check my Twitter for updates.