A reinforcement learning project designed to learn and complete the original Super Mario Bros. for the Nintendo Entertainment System using various models from the RLLib library from Ray.
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.