facebookresearch/ReAgent

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

3712 stars · 529 forks observed · 2026-08-28

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

ReAgent is an open-source end-to-end platform for applied reinforcement learning developed at Facebook, built in Python with PyTorch for training and TorchScript for serving. It supports deep RL algorithms, contextual bandits, and counterfactual policy evaluation for large-scale offline decision-making tasks. The project is officially archived and no longer maintained, with users directed to its successor Pearl.

Use cases

• train deep reinforcement learning policies on logged batch data
• run contextual bandit experiments like LinUCB and Thompson sampling
• evaluate new policies offline with counterfactual estimators before deployment
• build RL-based recommender systems with SlateQ or Seq2Slate
• serve trained RL models in production via TorchScript

When to choose

• you need an offline/batch RL platform for large-scale recommendation or optimization without a simulator
• you want both bandit algorithms and deep RL in one framework
• you need counterfactual policy evaluation (doubly robust, MAGIC) on logged data

When to avoid

• you need an actively maintained library - ReAgent is archived and unsupported
• you want production-ready RL tooling today - use its successor Pearl instead
• you need online RL with a simulator environment rather than offline batch training

Facets

framework · maturity abandoned

machine-learning reinforcement-learning benchmarking reinforcement-learning machine-learning large-language-models python pytorch contextual-bandits counterfactual-evaluation recommender-systems offline-rl torchscript facebook-research archived linux docker gpu

Member repositories

For agents

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