PyTorchRL Documentation

Getting started:

• Motivation
• Installation
  • Installing Anaconda or Miniconda
  • Installing PyTorchRL library

PyTorchRL API:

• Agent Components
  • Actors
  • Algorithms
  • Available Environments
  • Environment Vectors
  • Storage
• Training Components
  • Scheme
  • Learner

Tutorials:

• Breaking down PyTorchRL
  • 1. Starting a training script
  • 2. Defining a vectorized environment
  • 3. Defining a RL agent
  • 4. Customizing training scheme
  • 5. Executing training loop
  • 6. Checking results
• Create a custom environment

Code examples:

• Simplified Code Examples
  • Train Agents
  • Advanced Training Config Changes
  • Config
  • Available Algorithms
• Unity 3D Obstacle Tower Environment
  • The Environment
  • The Agent
  • The Scheme
  • Training
  • Results