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

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.

3. 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.

4. 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.

5. 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()

6. 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.

7. 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]

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