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.
- 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
Validation:
from stable_baselines3.common.env_checker import check_env
check_env(env, warn=True)
See scripts/custom_env_template.py for a complete custom environment template and references/custom_environments.md for comprehensive guidance.
- Vectorized Environments
Purpose: Vectorized environments run multiple environment instances in parallel, accelerating training and enabling certain wrappers (frame-stacking, normalization).
Types:
DummyVecEnv: Sequential execution on current process (for lightweight environments) SubprocVecEnv: Parallel execution across processes (for compute-heavy environments)
Quick Setup:
from stable_baselines3.common.env_util import make_vec_env
Create 4 parallel environments
env = make_vec_env("CartPole-v1", n_envs=4, vec_env_cls=SubprocVecEnv)
model = PPO("MlpPolicy", env, verbose=1) model.learn(total_timesteps=25000)
Off-Policy Optimization: When using multiple environments with off-policy algorithms (SAC, TD3, DQN), set gradient_steps=-1 to perform one gradient update per environment step, balancing wall-clock time and sample efficiency.
API Differences:
reset() returns only observations (info available in vec_env.reset_infos) step() returns 4-tuple: (obs, rewards, dones, infos) not 5-tuple Environments auto-reset after episodes Terminal observations available via infos[env_idx]["terminal_observation"]
See references/vectorized_envs.md for detailed information on wrappers and advanced usage.
- Callbacks for Monitoring and Control
Purpose: Callbacks enable monitoring metrics, saving checkpoints, implementing early stopping, and custom training logic without modifying core algorithms.
Common Callbacks:
EvalCallback: Evaluate periodically and save best model CheckpointCallback: Save model checkpoints at intervals StopTrainingOnRewardThreshold: Stop when target reward reached ProgressBarCallback: Display training progress with timing
Custom Callback Structure:
from stable_baselines3.common.callbacks import BaseCallback
class CustomCallback(BaseCallback): def _on_training_start(self): # Called before first rollout pass
def _on_step(self):
# Called after each environment step
# Return False to stop training
return True
def _on_rollout_end(self):
# Called at end of rollout
pass
Available Attributes:
self.model: The RL algorithm instance self.num_timesteps: Total environment steps self.training_env: The training environment
Chaining Callbacks:
from stable_baselines3.common.callbacks import CallbackList
callback = CallbackList([eval_callback, checkpoint_callback, custom_callback]) model.learn(total_timesteps=10000, callback=callback)
See references/callbacks.md for comprehensive callback documentation.
- Model Persistence and Inspection
Saving and Loading:
Save model
model.save("model_name")
Save normalization statistics (if using VecNormalize)
vec_env.save("vec_normalize.pkl")
Load model
model = PPO.load("model_name", env=env)
Load normalization statistics
vec_env = VecNormalize.load("vec_normalize.pkl", vec_env)
Parameter Access:
Get parameters
params = model.get_parameters()
Set parameters
model.set_parameters(params)
Access PyTorch state dict
state_dict = model.policy.state_dict()
- Evaluation and Recording
Evaluation:
from stable_baselines3.common.evaluation import evaluate_policy
mean_reward, std_reward = evaluate_policy( model, env, n_eval_episodes=10, deterministic=True )
Video Recording:
from stable_baselines3.common.vec_env import VecVideoRecorder
Wrap environment with video recorder
env = VecVideoRecorder( env, "videos/", record_video_trigger=lambda x: x % 2000 == 0, video_length=200 )
See scripts/evaluate_agent.py for a complete evaluation and recording template.
- Advanced Features
Learning Rate Schedules:
def linear_schedule(initial_value): def func(progress_remaining): # progress_remaining goes from 1 to 0 return progress_remaining * initial_value return func
model = PPO("MlpPolicy", env, learning_rate=linear_schedule(0.001))
Multi-Input Policies (Dict Observations):
model = PPO("MultiInputPolicy", env, verbose=1)
Use when observations are dictionaries (e.g., combining images with sensor data).
Hindsight Experience Replay:
from stable_baselines3 import SAC, HerReplayBuffer
model = SAC( "MultiInputPolicy", env, replay_buffer_class=HerReplayBuffer, replay_buffer_kwargs=dict( n_sampled_goal=4, goal_selection_strategy="future", ), )
TensorBoard Integration:
model = PPO("MlpPolicy", env, tensorboard_log="./tensorboard/") model.learn(total_timesteps=10000)
Workflow Guidance
Starting a New RL Project:
Define the problem: Identify observation space, action space, and reward structure Choose algorithm: Use references/algorithms.md for selection guidance Create/adapt environment: Use scripts/custom_env_template.py if needed Validate environment: Always run check_env() before training Set up training: Use scripts/train_rl_agent.py as starting template Add monitoring: Implement callbacks for evaluation and checkpointing Optimize performance: Consider vectorized environments for speed Evaluate and iterate: Use scripts/evaluate_agent.py for assessment
Common Issues:
Memory errors: Reduce buffer_size for off-policy algorithms or use fewer parallel environments Slow training: Consider SubprocVecEnv for parallel environments Unstable training: Try different algorithms, tune hyperparameters, or check reward scaling Import errors: Ensure stable_baselines3 is installed: uv pip install stable-baselines3[extra] Resources scripts/ train_rl_agent.py: Complete training script template with best practices evaluate_agent.py: Agent evaluation and video recording template custom_env_template.py: Custom Gym environment template references/ algorithms.md: Detailed algorithm comparison and selection guide custom_environments.md: Comprehensive custom environment creation guide callbacks.md: Complete callback system reference vectorized_envs.md: Vectorized environment usage and wrappers Installation
Basic installation
uv pip install stable-baselines3
With extra dependencies (Tensorboard, etc.)
uv pip install stable-baselines3[extra]