BigHugger
sk Skill · dralkh

stable-baselines3

Production-ready reinforcement learning algorithms (PPO, SAC, DQN, TD3, DDPG, A2C) with scikit-learn-like API. Use for standard RL experiments, quick prototyping, and well-documented algorithm implementations. Best for single-agent RL with Gymnasium environments. For high-performance parallel training, multi-agent systems, or custom vectorized environments, use pufferlib instead.

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Connections
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pythonbashTypeScript
Host repository
dralkh/iktinah
Version
1.1
Allowed tools
Read Write Edit Bash
Compatible with
Requires Python 3.10+, PyTorch >= 2.3, and stable-baselines3 2.8+. Gymnasium environments, optional extras for TensorBoard and Atari (ale-py).
Licence
MIT license
Host stars
78
Host language
TypeScript