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User Guide

  • Installation
  • Getting Started
  • Reinforcement Learning Tips and Tricks
  • Reinforcement Learning Resources
  • RL Algorithms
  • Examples
  • Vectorized Environments
  • Policy Networks
  • Using Custom Environments
  • Callbacks
  • Tensorboard Integration
  • Integrations
  • RL Baselines3 Zoo
  • SB3 Contrib
  • Stable Baselines Jax (SBX)
  • Plotting
  • Imitation Learning
  • Migrating from Stable-Baselines
  • Dealing with NaNs and infs
  • Developer Guide
  • On saving and loading
  • Exporting models

RL Algorithms

  • Base RL Class
  • A2C
  • DDPG
  • DQN
  • HER
  • PPO
  • SAC
  • TD3

Common

  • Atari Wrappers
  • Environments Utils
  • Custom Environments
  • Probability Distributions
  • Evaluation Helper
  • Gym Environment Checker
  • Monitor Wrapper
  • Logger
  • Action Noise
  • Utils

Misc

  • Changelog
  • Projects
Stable Baselines3
  • Reinforcement Learning Resources
  • View page source

Reinforcement Learning Resources

Stable-Baselines3 assumes that you already understand the basic concepts of Reinforcement Learning (RL).

However, if you want to learn about RL, there are several good resources to get started:

  • OpenAI Spinning Up

  • The Deep Reinforcement Learning Course

  • David Silver’s course

  • RL102: From Tabular Q-Learning to Deep Q-Learning (DQN)

  • RL103: From Deep Q-Learning (DQN) to Soft Actor-Critic (SAC) and Beyond

  • Lilian Weng’s blog

  • Berkeley’s Deep RL Bootcamp

  • Berkeley’s Deep Reinforcement Learning course

  • Decisions & Dragons - FAQ for RL foundations

  • More resources

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