PARL is a scalable reinforcement learning framework built on top of PaddlePaddle. It focuses on modularity and ease of use, supporting distributed training and a variety of RL algorithms.

Features

  • Modular design for easy algorithm customization and extension
  • Supports distributed training for large-scale experiments
  • Includes a variety of state-of-the-art RL algorithms
  • Compatible with PaddlePaddle for high-performance training
  • Built-in training loops and evaluation pipelines

Project Samples

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License

Apache License V2.0

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Additional Project Details

Programming Language

Python

Related Categories

Python Reinforcement Learning Frameworks

Registered

2025-03-13