alterlab-stable-baselines3

Stable Baselines3

Overview

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.

Core Capabilities

1. Training RL Agents

Basic Training Pattern:

import gymnasium as gym
from stable_baselines3 import PPO

# Create environment
env = gym.make("CartPole-v1")

# Initialize agent
model = PPO("MlpPolicy", env, verbose=1)

# Train the agent
model.learn(total_timesteps=10000)

# Save the model
model.save("ppo_cartpole")

# Load the model (without prior instantiation)
model = PPO.load("ppo_cartpole", env=env)

Important Notes:

• total_timesteps 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/train_rl_agent.py for a complete training template with best practices.

2. Custom Environments

Requirements: Custom environments must inherit from gymnasium.Env and implement:

• __init__(): Define action_space and observation_space
• reset(seed, options): Return initial observation and info dict
• step(action): Return observation, reward, terminated, truncated, info
• render(): Visualization (optional)
• close(): Cleanup resources

Key Constraints:

• Image observations must be np.uint8 in range [0, 255]
• Use channel-first format when possible (channels, height, width)
• SB3 normalizes images automatically by dividing by 255
• Set normalize_images=False in policy_kwargs if pre-normalized
• SB3 does NOT support Discrete or MultiDiscrete spaces with start!=0