snake-ai-reinforcement

AI for Snake game trained from pixels using Deep Reinforcement Learning (DQN).

Contains the tools for training and observing the behavior of the agents, either in CLI or GUI mode.

Requirements

All modules require Python 3.6 or above. Note that support for Python 3.7 in TensorFlow is experimental at the time of writing, and requirements may need to be updated as new official versions get released.

Training on GPU is supported but disabled by default. If you have CUDA and would like to use a GPU, use the GPU version of TensorFlow by changing tensorflow to tensorflow-gpu in the requirements file.

To install all Python dependencies, run:

$ make deps

Pre-Trained Models

You can find a few pre-trained DQN agents on the Releases page. Pass the model file to the play.py front-end script (see play.py -h for help).

dqn-10x10-blank.model

An agent pre-trained on a blank 10x10 level (snakeai/levels/10x10-blank.json).
dqn-10x10-obstacles.model

An agent pre-trained on a 10x10 level with obstacles (snakeai/levels/10x10-obstacles.json).

Training a DQN Agent

To train an agent using the default configuration, run:

$ make train

The trained model will be checkpointed during the training and saved as dqn-final.model afterwards.

Run train.py with custom arguments to change the level or the duration of the training (see train.py -h for help).

Playback

The behavior of the agent can be tested either in batch CLI mode where the agent plays a set of episodes and outputs summary statistics, or in GUI mode where you can see each individual step and action.

To test the agent in batch CLI mode, run the following command and check the generated .csv file:

$ make play

To use the GUI mode, run:

$ make play-gui

To play on your own using the arrow keys (I know you want to), run:

$ make play-human

Running Unit Tests

$ make test