API

API#

General Deep Actor Critic architecture.

General Deep Actor Critic architecture.#

The figure below illustrates the core design of ObjectRL.

Overview#

ObjectRL’s API is organized into components that allow for flexible experimentation and development of reinforcement learning algorithms. Key modules include:

  • Agents: Implementations of various RL agents. Currently, it includes the base Agent class.

  • Configurations: Flexible, dataclass-based configurations that control hyperparameters and architectural choices.

  • Experiments: Scripts and utilities for running training and evaluation workflows.

  • Loggers: Tools for experiment tracking and result visualization.

  • Models: Algorithms for actors, critics, and actor-critics.

  • Nets: Building blocks such as policy and value networks.

  • Replay Buffers: Experience storage.

  • Utils: Helper functions, data structures, and network utilities.

This structure is designed to separate concerns clearly, enabling rapid prototyping and easy customization.

Getting Started#

If you’re new to ObjectRL, check out the Getting Started page for installation instructions and quick-start tutorials.

Further Reading#

For practical use cases and advanced examples, see the Examples page, and for in-depth algorithm references, stay in this section.

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