rail-berkeley/softlearning

Health v2 · maintenance only

Flags: no_releases no_license

Adoption not part of the score

1437 stars · 249 forks observed · 2026-08-28

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

Softlearning is a deep reinforcement learning toolbox for training maximum entropy policies in continuous domains, and the official implementation of the Soft Actor-Critic (SAC) algorithm. It is built on TensorFlow/Keras with Ray Tune for experiment orchestration and distributed cloud training.

Use cases

• train soft actor-critic agents on continuous control tasks
• run maximum entropy reinforcement learning experiments
• reproduce official SAC research results
• launch distributed RL training on cloud services with Ray
• train RL policies for robotic locomotion and manipulation
• benchmark off-policy deep RL algorithms on MuJoCo environments

When to choose

• you need the canonical, research-grade SAC implementation
• you work with continuous-domain control and MuJoCo environments
• you want Ray-based scaling of RL experiments to cloud infrastructure
• you prefer TensorFlow/Keras for RL model definitions

When to avoid

• you want a PyTorch implementation - use rlkit or other SAC ports instead
• you need actively maintained software with recent updates
• you want RL for discrete action spaces
• you cannot obtain a MuJoCo license or prefer license-free simulators

Facets

library · maturity maintenance

reinforcement-learning machine-learning deep-learning cli reinforcement-learning machine-learning robotics deep-learning python soft-actor-critic maximum-entropy-rl tensorflow ray-tune mujoco continuous-control off-policy linux docker gpu

Member repositories

For agents

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