PARL is a high-performance distributed training framework for reinforcement learning developed by PaddlePaddle. It provides abstractions for Model, Algorithm, and Agent to build RL agents that perform complex tasks. The framework supports large-scale parallelization across thousands of CPUs and multiple GPUs, and includes implementations of influential RL algorithms that can be reproduced and adapted to new tasks.