The Python Research Toolkit: Reinforcement Learning (prt-rl)

The Python Research Toolkit: Reinforcement Learning (prt-rl)#

prt-rl is part of the broader Python Research Toolkit ecosystem and provides a clean, mathematically grounded collection of reinforcement learning algorithms.
Its primary goal is clarity, pedagogy, and research exploration—not raw performance.

This library is designed for researchers, students, and practitioners who want to understand why RL algorithms work, how their mathematics map to code, and what practical implementation details matter in real systems.

Unlike high-performance libraries such as TorchRL, RLlib, and skrl, prt-rl focuses on transparency, composability, and conceptual depth. Every algorithm is implemented with an emphasis on readability, modularity, and annotated code that highlights both the underlying equations and the implementation tips that make them work in practice.

Main Contributions#

  • Composable Architecture with Minimal Inheritance
    Designed around simple, reusable building blocks so new algorithms can be created easily.
    Emphasis is placed on composition over inheritance to reduce architectural overhead and promote extensibility.

  • Broad Coverage of RL Paradigms
    Includes state-of-the-art model-free algorithms (e.g., PPO, SAC, DQN, TD3) as well as support for other branches of RL such as model-based RL, meta-RL, and multi-agent RL.

  • Mathematically Faithful Implementations
    Algorithms follow their original mathematical derivations closely.
    Code is extensively commented with explanations, references to key papers, and practical “tips and tricks” used in modern RL research.

⚠️ Note: This repository is under active development. APIs, file structure, and module organization may change as the project evolves. Backward compatibility is not guaranteed until version 1.0.

Github Repository: GavinStrunk/prt-rl

To begin using this library follow the Installation instructions and then see the Examples.

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