OpenAI's Gym is an open source toolkit containing several environments which can be used to compare reinforcement learning algorithms and techniques in a consistent and repeatable manner, easily allowing developers to benchmark their solutions.

This repository aims to create a simple one-stop location for testing reinforcement learning models without worrying about configuring or maintaining the environment. Featuring extensive command-line parameters, various tweaks to settings can easily be made to determine an optimal configuration for a particular environment and model.

After cloning the repository, it is highly recommended to install a virtual environment (such as virtualenv) or Anaconda to isolate the dependencies of this project with other system dependencies.

To install virtualenv, simply run

pip install virtualenv

Once installed, a new virtual environment can be created by running

virtualenv env

This will create a virtual environment in the env directory in the current working directory. To change the location and/or name of the environment directory, change env to the desired path in the command above.

To enter the virtual environment, run

source env/bin/activate

You should see (env) at the beginning of the terminal prompt, indicating the environment is active. Again, replace env with your desired directory name.

To get out of the environment, simply run

deactivate

While the virtual environment is active, install the required dependencies by running

pip -r requirements.txt

This will install all of the dependencies at specific versions to ensure they are compatible with one another.

To train a model, use the train.py script and specify any parameters that need to be changed, such as the environment or epsilon decay factors. A list of the default values for every parameters can be found by running

python train.py --help

If you desire to run with the default settings, execute the script directly with

python train.py

The script will train the default environment over a set number of episodes and display the training progress after the conclusion of every episode. The updates indicate the episode number, the reward for the current episode, the best reward the model has achieved so far, a rolling average of the previous 100 episode rewards, and the current value for epsilon.

Any time the model reaches a new best rolling average, the current model weights are saved as a .dat file with the environment's name (such as PongNoFrameskip-v4.dat). This saved model will overwrite any existing model weight files for the same environment.