Note

Ray 2.10.0 introduces the alpha stage of RLlib’s “new API stack”. The team is currently transitioning algorithms, example scripts, and documentation to the new code base throughout the subsequent minor releases leading up to Ray 3.0.

See here for more details on how to activate and use the new API stack.

Ray RLlib API

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This section contains an overview of RLlib’s package- and API reference. If you think there is anything missing, please open an issue on Github.

• Algorithms
  • Algorithm Configuration API
  • Building Custom Algorithm Classes
  • Algorithm API
• Environments
  • Environment API Reference
• Policy API
  • Building Custom Policy Classes
  • Base Policy classes
  • Making models
  • Inference
  • Computing, processing, and applying gradients
  • Updating the Policy’s model
  • Loss, Logging, optimizers, and trajectory processing
  • Saving and restoring
  • Recurrent Policies
  • Miscellaneous
• Model APIs
  • Base Model classes
  • Feed Forward methods
  • Recurrent Models API
  • Acessing variables
  • Customization
• Catalog API
  • Basic usage
  • Advanced usage
• RLModule API
  • RL Module specifications and configurations
  • RL Module API
  • Multi Agent RL Module API
• LearnerGroup API
  • Configuring a LearnerGroup and Learner Workers
  • Constructing a LearnerGroup
• Learner API
  • Constructing a Learner
  • Performing Updates
  • Computing Losses
  • Configuring Optimizers
  • Gradient Computation
  • Saving, Loading, Checkpointing, and Restoring States
  • Adding and Removing Modules
• Sampling the Environment or offline data
  • RolloutWorker API
  • EnvRunner API
  • EnvRunnerGroup API
  • Sampler API
  • Policy Map API
  • Sample batch API
  • MultiAgent batch API
• Replay Buffer API
  • Replay Buffer Base Classes
  • Public Methods
  • Multi Agent Buffers
  • Utility Methods
• RLlib Utilities
  • Exploration API
  • Scheduler API
  • Training Operations Utilities
  • Framework Utilities
• External Application API
  • PolicyClient
  • PolicyServerInput