rail-berkeley/rlkit

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2932 stars · 573 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

RLkit is a PyTorch-based reinforcement learning framework and algorithm collection from UC Berkeley RAIL. It provides reference implementations of algorithms such as SAC, TD3, DQN, HER, AWAC, IQL, and Skew-Fit with example scripts.

Use cases

• implement soft actor-critic experiments in pytorch
• reproduce reinforcement learning research papers
• train goal-conditioned policies with hindsight experience replay
• run offline RL with implicit q-learning
• learn how RL algorithms like TD3 and DQN are implemented
• prototype new deep RL algorithms on top of an existing framework

When to choose

• you need reference PyTorch implementations of classic and offline RL algorithms
• you are doing RL research and want readable, hackable algorithm code
• you want example scripts for gym-style environments

When to avoid

• you need a production RL training service with distributed scaling
• you want a high-level API like Stable-Baselines3 with many maintained environments
• you need actively developed features or recent RL algorithm support

Facets

library · maturity maintenance

reinforcement-learning machine-learning benchmarking reinforcement-learning machine-learning artificial-intelligence python pytorch research off-policy-learning goal-conditioned-rl offline-rl

Member repositories

For agents

markdown · JSON · MCP: product_card(name="rail-berkeley/rlkit")