PaddlePaddle/PARL

Health v2 · maintenance only

Adoption not part of the score

3453 stars · 812 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

PARL is a flexible, high-performance reinforcement learning framework built on PaddlePaddle, providing Model/Algorithm/Agent abstractions and reproducible implementations of influential RL algorithms. Its xparl component enables large-scale distributed training across thousands of CPUs and multiple GPUs with a simple decorator-based API.

Use cases

• train reinforcement learning agents at scale
• parallelize RL training across thousands of CPUs
• reproduce classic RL algorithms like DQN and PPO
• build custom RL algorithms by inheriting base classes
• apply GPU-accelerated RL to autonomous driving simulation
• run distributed multi-GPU reinforcement learning experiments

When to choose

• you need scalable, distributed RL training with minimal code changes
• you want reproducible implementations of standard RL algorithms
• you are already using the PaddlePaddle ecosystem
• you need to parallelize environment data collection across many CPU workers

When to avoid

• you prefer PyTorch- or TensorFlow-based RL libraries like Stable-Baselines3 or RLlib
• you only need small-scale single-machine RL without parallelization
• you need supervised or self-supervised learning rather than RL

Facets

framework · maturity active

reinforcement-learning machine-learning llm-training reinforcement-learning machine-learning deep-learning python cross-platform distributed-training parallelization paddlepaddle rl-algorithms xparl linux gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="PaddlePaddle/PARL")