stable-baselines3

原文涵盖的主题

• Stable Baselines3
• Overview
• Core Capabilities
• 1. Training RL Agents
• 2. Custom Environments
• 3. Vectorized Environments
• 4. Callbacks for Monitoring and Control
• 5. Model Persistence and Inspection

Stable Baselines3 (SB3) is a PyTorch-based library providing reliable implementations of reinforcement learning algorithms. This skill provides comprehensive guidance for training RL agents, creating custom environments, implementing callbacks, and optimizing training workflows using SB3's unified API. Basic Training Pattern: Important Notes: totaltimesteps is a lower bound; actual training may exceed this due to batch collection Use model.load() as a static method, not on an existing instance The replay buffer is NOT saved with the model to save space Algorithm Selection: Use references/algorithms.md for detailed algorithm characteristics and selection guidance. Quick reference: PPO/A2C: General-purpose, supports all action space types, good for multiprocessing SAC/TD3: Continuous control, off-policy, sample-efficient DQN: Discrete actions, off-policy HER: Goal-conditioned tasks See scripts/trainrlagent.py for a complete training template with best practices. Requirements: Custom environments must inherit from gymnasium.Env and implement: init(): Define actionspace and observationspace reset(seed, options): Return initial observation and info dict step(action): Return observation,

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