Use this skill for reinforcement learning tasks including training RL agents (PPO, SAC, DQN, TD3, DDPG, A2C, etc.), creating custom Gym environments, implementing callbacks for monitoring and control, using vectorized environments for parallel training, and integrating with deep RL workflows. This skill should be used when users request RL algorithm implementation, agent training, environment design, or RL experimentation.
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Use this skill for reinforcement learning tasks including training RL agents (PPO, SAC, DQN, TD3, DDPG, A2C, etc.), creating custom Gym environments, implementing callbacks for monitoring and control, using vectorized environments for parallel training, and integrating with deep RL workflows. This skill should be used when users request RL algorithm implementation, agent training, environment design, or RL experimentation.
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
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
classCustomCallback(BaseCallback):
def_on_training_start(self):
# Called before first rolloutpassdef_on_step(self):
# Called after each environment step# Return False to stop trainingreturnTruedef_on_rollout_end(self):
# Called at end of rolloutpass
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()
