facebookresearch/Pearl

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3024 stars · 205 forks observed · 2026-08-28

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

Pearl is a production-ready reinforcement learning agent library developed by Meta's Applied Reinforcement Learning team. It provides modular PyTorch-based components for building RL agents that handle long-term cumulative feedback, limited observability, sparse rewards, and stochastic environments.

Use cases

• train reinforcement learning agents in python
• build production RL agents for dynamic environments
• experiment with deep RL algorithms like DQN and SAC
• handle sparse rewards and partial observability in RL
• save and load trained agent state dicts
• research sequential decision-making problems

When to choose

• you need a modular PyTorch RL library with production ambitions
• your environment has limited observability, sparse feedback, or high stochasticity
• you want serialization of agent components via torch.save/torch.load
• you prefer an MIT-licensed library backed by an active research team

When to avoid

• you need supervised learning or standard ML pipelines without sequential decision-making
• you want a mature, battle-tested framework like Stable-Baselines3 with a large community
• you need non-Python or non-PyTorch support

Facets

library · maturity active

reinforcement-learning machine-learning agent-framework reinforcement-learning machine-learning artificial-intelligence python pytorch meta-ai production-rl deep-rl decision-making ai-agents

Member repositories

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markdown · JSON · MCP: product_card(name="facebookresearch/Pearl")