Workout is an API to import and use OpenAI-Gym's environment with PyTorch effortlessly
PyTorch: Flexible framework to implement deep neural networks and has better GPU integration
OpenAI-Gym: Provides extensive and varied Reinforcement Learning environments to use readily
However, the integration between two is not very extensive. Many works have been done to implement
deep network based Reinforcement Learning algorithms using PyTorch seperately, then transfer the whole control
to Gym's environment to estimate reward function, state of the system, possible actions for next step, etc.,
and pass it again to PyTorch's model. Therefore, to avoid such complications, Workout provides a higher level of abstraction to the Gym's environment, providing an interface to make it more PyTorch oriented. By doing so,
the users shall effortlessly use Gym's environment without affecting PyTorch's syntactic sugar. Also, the
translation to PyTorch codebase would improve the uniformity of the underlying kernel and helps heavily in
parallelization using GPUs.
Workout provides several classes that acts as an interface between Gym and Pytorch. The package is centered towards Q-Learning. So it will allow users to define their own Policies, Models, preprocessing the inputs to and outputs from the model and define training loops or use the default ones.