These examples are only to demonstrate the use of the library and its functions, and the trained agents may not solve the environments. Optimized hyperparameters can be found in the RL Zoo repository.

Try it online with Colab Notebooks!

All the following examples can be executed online using Google colab colab notebooks:

Basic Usage: Training, Saving, Loading

In the following example, we will train, save and load a DQN model on the Lunar Lander environment.


Lunar Lander Environment


LunarLander requires the python package box2d. You can install it using apt install swig and then pip install box2d box2d-kengz


load method re-creates the model from scratch and should be called on the Algorithm without instantiating it first, e.g. model = DQN.load("dqn_lunar", env=env) instead of model = DQN(env=env) followed by model.load("dqn_lunar"). The latter will not work as load is not an in-place operation. If you want to load parameters without re-creating the model, e.g. to evaluate the same model with multiple different sets of parameters, consider using set_parameters instead.

import gymnasium as gym

from stable_baselines3 import DQN
from stable_baselines3.common.evaluation import evaluate_policy

# Create environment
env = gym.make("LunarLander-v2", render_mode="rgb_array")

# Instantiate the agent
model = DQN("MlpPolicy", env, verbose=1)
# Train the agent and display a progress bar
model.learn(total_timesteps=int(2e5), progress_bar=True)
# Save the agent
del model  # delete trained model to demonstrate loading

# Load the trained agent
# NOTE: if you have loading issue, you can pass `print_system_info=True`
# to compare the system on which the model was trained vs the current one
# model = DQN.load("dqn_lunar", env=env, print_system_info=True)
model = DQN.load("dqn_lunar", env=env)

# Evaluate the agent
# NOTE: If you use wrappers with your environment that modify rewards,
#       this will be reflected here. To evaluate with original rewards,
#       wrap environment in a "Monitor" wrapper before other wrappers.
mean_reward, std_reward = evaluate_policy(model, model.get_env(), n_eval_episodes=10)

# Enjoy trained agent
vec_env = model.get_env()
obs = vec_env.reset()
for i in range(1000):
    action, _states = model.predict(obs, deterministic=True)
    obs, rewards, dones, info = vec_env.step(action)

Multiprocessing: Unleashing the Power of Vectorized Environments


CartPole Environment

import gymnasium as gym

from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv, SubprocVecEnv
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.utils import set_random_seed

def make_env(env_id: str, rank: int, seed: int = 0):
    Utility function for multiprocessed env.

    :param env_id: the environment ID
    :param num_env: the number of environments you wish to have in subprocesses
    :param seed: the initial seed for RNG
    :param rank: index of the subprocess
    def _init():
        env = gym.make(env_id, render_mode="human")
        env.reset(seed=seed + rank)
        return env
    return _init

if __name__ == "__main__":
    env_id = "CartPole-v1"
    num_cpu = 4  # Number of processes to use
    # Create the vectorized environment
    vec_env = SubprocVecEnv([make_env(env_id, i) for i in range(num_cpu)])

    # Stable Baselines provides you with make_vec_env() helper
    # which does exactly the previous steps for you.
    # You can choose between `DummyVecEnv` (usually faster) and `SubprocVecEnv`
    # env = make_vec_env(env_id, n_envs=num_cpu, seed=0, vec_env_cls=SubprocVecEnv)

    model = PPO("MlpPolicy", vec_env, verbose=1)

    obs = vec_env.reset()
    for _ in range(1000):
        action, _states = model.predict(obs)
        obs, rewards, dones, info = vec_env.step(action)

Multiprocessing with off-policy algorithms


When using multiple environments with off-policy algorithms, you should update the gradient_steps parameter too. Set it to gradient_steps=-1 to perform as many gradient steps as transitions collected. There is usually a compromise between wall-clock time and sample efficiency, see this example in PR #439

import gymnasium as gym

from stable_baselines3 import SAC
from stable_baselines3.common.env_util import make_vec_env

vec_env = make_vec_env("Pendulum-v0", n_envs=4, seed=0)

# We collect 4 transitions per call to `ènv.step()`
# and performs 2 gradient steps per call to `ènv.step()`
# if gradient_steps=-1, then we would do 4 gradients steps per call to `ènv.step()`
model = SAC("MlpPolicy", vec_env, train_freq=1, gradient_steps=2, verbose=1)

Dict Observations

You can use environments with dictionary observation spaces. This is useful in the case where one can’t directly concatenate observations such as an image from a camera combined with a vector of servo sensor data (e.g., rotation angles). Stable Baselines3 provides SimpleMultiObsEnv as an example of this kind of setting. The environment is a simple grid world, but the observations for each cell come in the form of dictionaries. These dictionaries are randomly initialized on the creation of the environment and contain a vector observation and an image observation.

from stable_baselines3 import PPO
from stable_baselines3.common.envs import SimpleMultiObsEnv

# Stable Baselines provides SimpleMultiObsEnv as an example environment with Dict observations
env = SimpleMultiObsEnv(random_start=False)

model = PPO("MultiInputPolicy", env, verbose=1)

Callbacks: Monitoring Training


We recommend reading the Callback section

You can define a custom callback function that will be called inside the agent. This could be useful when you want to monitor training, for instance display live learning curves in Tensorboard or save the best agent. If your callback returns False, training is aborted early.

import os

import gymnasium as gym
import numpy as np
import matplotlib.pyplot as plt

from stable_baselines3 import TD3
from stable_baselines3.common import results_plotter
from stable_baselines3.common.monitor import Monitor
from stable_baselines3.common.results_plotter import load_results, ts2xy, plot_results
from stable_baselines3.common.noise import NormalActionNoise
from stable_baselines3.common.callbacks import BaseCallback

class SaveOnBestTrainingRewardCallback(BaseCallback):
    Callback for saving a model (the check is done every ``check_freq`` steps)
    based on the training reward (in practice, we recommend using ``EvalCallback``).

    :param check_freq:
    :param log_dir: Path to the folder where the model will be saved.
      It must contains the file created by the ``Monitor`` wrapper.
    :param verbose: Verbosity level: 0 for no output, 1 for info messages, 2 for debug messages
    def __init__(self, check_freq: int, log_dir: str, verbose: int = 1):
        self.check_freq = check_freq
        self.log_dir = log_dir
        self.save_path = os.path.join(log_dir, "best_model")
        self.best_mean_reward = -np.inf

    def _init_callback(self) -> None:
        # Create folder if needed
        if self.save_path is not None:
            os.makedirs(self.save_path, exist_ok=True)

    def _on_step(self) -> bool:
        if self.n_calls % self.check_freq == 0:

          # Retrieve training reward
          x, y = ts2xy(load_results(self.log_dir), "timesteps")
          if len(x) > 0:
              # Mean training reward over the last 100 episodes
              mean_reward = np.mean(y[-100:])
              if self.verbose >= 1:
                print(f"Num timesteps: {self.num_timesteps}")
                print(f"Best mean reward: {self.best_mean_reward:.2f} - Last mean reward per episode: {mean_reward:.2f}")

              # New best model, you could save the agent here
              if mean_reward > self.best_mean_reward:
                  self.best_mean_reward = mean_reward
                  # Example for saving best model
                  if self.verbose >= 1:
                    print(f"Saving new best model to {self.save_path}")

        return True

# Create log dir
log_dir = "tmp/"
os.makedirs(log_dir, exist_ok=True)

# Create and wrap the environment
env = gym.make("LunarLanderContinuous-v2")
env = Monitor(env, log_dir)

# Add some action noise for exploration
n_actions = env.action_space.shape[-1]
action_noise = NormalActionNoise(mean=np.zeros(n_actions), sigma=0.1 * np.ones(n_actions))
# Because we use parameter noise, we should use a MlpPolicy with layer normalization
model = TD3("MlpPolicy", env, action_noise=action_noise, verbose=0)
# Create the callback: check every 1000 steps
callback = SaveOnBestTrainingRewardCallback(check_freq=1000, log_dir=log_dir)
# Train the agent
timesteps = 1e5
model.learn(total_timesteps=int(timesteps), callback=callback)

plot_results([log_dir], timesteps, results_plotter.X_TIMESTEPS, "TD3 LunarLander")

Callbacks: Evaluate Agent Performance

To periodically evaluate an agent’s performance on a separate test environment, use EvalCallback. You can control the evaluation frequency with eval_freq to monitor your agent’s progress during training.

import os
import gymnasium as gym

from stable_baselines3 import SAC
from stable_baselines3.common.callbacks import EvalCallback
from stable_baselines3.common.env_util import make_vec_env

env_id = "Pendulum-v1"
n_training_envs = 1
n_eval_envs = 5

# Create log dir where evaluation results will be saved
eval_log_dir = "./eval_logs/"
os.makedirs(eval_log_dir, exist_ok=True)

# Initialize a vectorized training environment with default parameters
train_env = make_vec_env(env_id, n_envs=n_training_envs, seed=0)

# Separate evaluation env, with different parameters passed via env_kwargs
# Eval environments can be vectorized to speed up evaluation.
eval_env = make_vec_env(env_id, n_envs=n_eval_envs, seed=0,

# Create callback that evaluates agent for 5 episodes every 500 training environment steps.
# When using multiple training environments, agent will be evaluated every
# eval_freq calls to train_env.step(), thus it will be evaluated every
# (eval_freq * n_envs) training steps. See EvalCallback doc for more information.
eval_callback = EvalCallback(eval_env, best_model_save_path=eval_log_dir,
                              log_path=eval_log_dir, eval_freq=max(500 // n_training_envs, 1),
                              n_eval_episodes=5, deterministic=True,

model = SAC("MlpPolicy", train_env)
model.learn(5000, callback=eval_callback)

Atari Games


Trained A2C agent on Breakout


Pong Environment

Training a RL agent on Atari games is straightforward thanks to make_atari_env helper function. It will do all the preprocessing and multiprocessing for you. To install the Atari environments, run the command pip install gymnasium[atari,accept-rom-license] to install the Atari environments and ROMs, or install Stable Baselines3 with pip install stable-baselines3[extra] to install this and other optional dependencies.

from stable_baselines3.common.env_util import make_atari_env
from stable_baselines3.common.vec_env import VecFrameStack
from stable_baselines3 import A2C

# There already exists an environment generator
# that will make and wrap atari environments correctly.
# Here we are also multi-worker training (n_envs=4 => 4 environments)
vec_env = make_atari_env("PongNoFrameskip-v4", n_envs=4, seed=0)
# Frame-stacking with 4 frames
vec_env = VecFrameStack(vec_env, n_stack=4)

model = A2C("CnnPolicy", vec_env, verbose=1)

obs = vec_env.reset()
while True:
    action, _states = model.predict(obs, deterministic=False)
    obs, rewards, dones, info = vec_env.step(action)

PyBullet: Normalizing input features

Normalizing input features may be essential to successful training of an RL agent (by default, images are scaled but not other types of input), for instance when training on PyBullet environments. For that, a wrapper exists and will compute a running average and standard deviation of input features (it can do the same for rewards).


you need to install pybullet with pip install pybullet

import os
import gymnasium as gym
import pybullet_envs

from stable_baselines3.common.vec_env import DummyVecEnv, VecNormalize
from stable_baselines3 import PPO

# Note: pybullet is not compatible yet with Gymnasium
# you might need to use `import rl_zoo3.gym_patches`
# and use gym (not Gymnasium) to instantiate the env
# Alternatively, you can use the MuJoCo equivalent "HalfCheetah-v4"
vec_env = DummyVecEnv([lambda: gym.make("HalfCheetahBulletEnv-v0")])
# Automatically normalize the input features and reward
vec_env = VecNormalize(vec_env, norm_obs=True, norm_reward=True,

model = PPO("MlpPolicy", vec_env)

# Don't forget to save the VecNormalize statistics when saving the agent
log_dir = "/tmp/"
model.save(log_dir + "ppo_halfcheetah")
stats_path = os.path.join(log_dir, "vec_normalize.pkl")

# To demonstrate loading
del model, vec_env

# Load the saved statistics
vec_env = DummyVecEnv([lambda: gym.make("HalfCheetahBulletEnv-v0")])
vec_env = VecNormalize.load(stats_path, vec_env)
#  do not update them at test time
vec_env.training = False
# reward normalization is not needed at test time
vec_env.norm_reward = False

# Load the agent
model = PPO.load(log_dir + "ppo_halfcheetah", env=vec_env)

Hindsight Experience Replay (HER)

For this example, we are using Highway-Env by @eleurent.


The highway-parking-v0 environment.

The parking env is a goal-conditioned continuous control task, in which the vehicle must park in a given space with the appropriate heading.


The hyperparameters in the following example were optimized for that environment.

import gymnasium as gym
import highway_env
import numpy as np

from stable_baselines3 import HerReplayBuffer, SAC, DDPG, TD3
from stable_baselines3.common.noise import NormalActionNoise

env = gym.make("parking-v0")

# Create 4 artificial transitions per real transition
n_sampled_goal = 4

# SAC hyperparams:
model = SAC(
    policy_kwargs=dict(net_arch=[256, 256, 256]),


# Load saved model
# Because it needs access to `env.compute_reward()`
# HER must be loaded with the env
env = gym.make("parking-v0", render_mode="human") # Change the render mode
model = SAC.load("her_sac_highway", env=env)

obs, info = env.reset()

# Evaluate the agent
episode_reward = 0
for _ in range(100):
    action, _ = model.predict(obs, deterministic=True)
    obs, reward, terminated, truncated, info = env.step(action)
    episode_reward += reward
    if terminated or truncated or info.get("is_success", False):
        print("Reward:", episode_reward, "Success?", info.get("is_success", False))
        episode_reward = 0.0
        obs, info = env.reset()

Learning Rate Schedule

All algorithms allow you to pass a learning rate schedule that takes as input the current progress remaining (from 1 to 0). PPO’s clip_range` parameter also accepts such schedule.

The RL Zoo already includes linear and constant schedules.

from typing import Callable

from stable_baselines3 import PPO

def linear_schedule(initial_value: float) -> Callable[[float], float]:
    Linear learning rate schedule.

    :param initial_value: Initial learning rate.
    :return: schedule that computes
      current learning rate depending on remaining progress
    def func(progress_remaining: float) -> float:
        Progress will decrease from 1 (beginning) to 0.

        :param progress_remaining:
        :return: current learning rate
        return progress_remaining * initial_value

    return func

# Initial learning rate of 0.001
model = PPO("MlpPolicy", "CartPole-v1", learning_rate=linear_schedule(0.001), verbose=1)
# By default, `reset_num_timesteps` is True, in which case the learning rate schedule resets.
# progress_remaining = 1.0 - (num_timesteps / total_timesteps)
model.learn(total_timesteps=10_000, reset_num_timesteps=True)

Advanced Saving and Loading

In this example, we show how to use a policy independently from a model (and how to save it, load it) and save/load a replay buffer.

By default, the replay buffer is not saved when calling model.save(), in order to save space on the disk (a replay buffer can be up to several GB when using images). However, SB3 provides a save_replay_buffer() and load_replay_buffer() method to save it separately.


For training model after loading it, we recommend loading the replay buffer to ensure stable learning (for off-policy algorithms). You also need to pass reset_num_timesteps=True to learn function which initializes the environment and agent for training if a new environment was created since saving the model.

from stable_baselines3 import SAC
from stable_baselines3.common.evaluation import evaluate_policy
from stable_baselines3.sac.policies import MlpPolicy

# Create the model and the training environment
model = SAC("MlpPolicy", "Pendulum-v1", verbose=1,

# train the model

# save the model

# the saved model does not contain the replay buffer
loaded_model = SAC.load("sac_pendulum")
print(f"The loaded_model has {loaded_model.replay_buffer.size()} transitions in its buffer")

# now save the replay buffer too

# load it into the loaded_model

# now the loaded replay is not empty anymore
print(f"The loaded_model has {loaded_model.replay_buffer.size()} transitions in its buffer")

# Save the policy independently from the model
# Note: if you don't save the complete model with `model.save()`
# you cannot continue training afterward
policy = model.policy

# Retrieve the environment
env = model.get_env()

# Evaluate the policy
mean_reward, std_reward = evaluate_policy(policy, env, n_eval_episodes=10, deterministic=True)

print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

# Load the policy independently from the model
saved_policy = MlpPolicy.load("sac_policy_pendulum")

# Evaluate the loaded policy
mean_reward, std_reward = evaluate_policy(saved_policy, env, n_eval_episodes=10, deterministic=True)

print(f"mean_reward={mean_reward:.2f} +/- {std_reward}")

Accessing and modifying model parameters

You can access model’s parameters via set_parameters and get_parameters functions, or via model.policy.state_dict() (and load_state_dict()), which use dictionaries that map variable names to PyTorch tensors.

These functions are useful when you need to e.g. evaluate large set of models with same network structure, visualize different layers of the network or modify parameters manually.

Policies also offers a simple way to save/load weights as a NumPy vector, using parameters_to_vector() and load_from_vector() method.

Following example demonstrates reading parameters, modifying some of them and loading them to model by implementing evolution strategy (es) for solving the CartPole-v1 environment. The initial guess for parameters is obtained by running A2C policy gradient updates on the model.

from typing import Dict

import gymnasium as gym
import numpy as np
import torch as th

from stable_baselines3 import A2C
from stable_baselines3.common.evaluation import evaluate_policy

def mutate(params: Dict[str, th.Tensor]) -> Dict[str, th.Tensor]:
    """Mutate parameters by adding normal noise to them"""
    return dict((name, param + th.randn_like(param)) for name, param in params.items())

# Create policy with a small network
model = A2C(
    policy_kwargs={"net_arch": [32]},

# Use traditional actor-critic policy gradient updates to
# find good initial parameters

# Include only variables with "policy", "action" (policy) or "shared_net" (shared layers)
# in their name: only these ones affect the action.
# NOTE: you can retrieve those parameters using model.get_parameters() too
mean_params = dict(
    (key, value)
    for key, value in model.policy.state_dict().items()
    if ("policy" in key or "shared_net" in key or "action" in key)

# population size of 50 invdiduals
pop_size = 50
# Keep top 10%
n_elite = pop_size // 10
# Retrieve the environment
vec_env = model.get_env()

for iteration in range(10):
    # Create population of candidates and evaluate them
    population = []
    for population_i in range(pop_size):
        candidate = mutate(mean_params)
        # Load new policy parameters to agent.
        # Tell function that it should only update parameters
        # we give it (policy parameters)
        model.policy.load_state_dict(candidate, strict=False)
        # Evaluate the candidate
        fitness, _ = evaluate_policy(model, vec_env)
        population.append((candidate, fitness))
    # Take top 10% and use average over their parameters as next mean parameter
    top_candidates = sorted(population, key=lambda x: x[1], reverse=True)[:n_elite]
    mean_params = dict(
            th.stack([candidate[0][name] for candidate in top_candidates]).mean(dim=0),
        for name in mean_params.keys()
    mean_fitness = sum(top_candidate[1] for top_candidate in top_candidates) / n_elite
    print(f"Iteration {iteration + 1:<3} Mean top fitness: {mean_fitness:.2f}")
    print(f"Best fitness: {top_candidates[0][1]:.2f}")

SB3 and ProcgenEnv

Some environments like Procgen already produce a vectorized environment (see discussion in issue #314). In order to use it with SB3, you must wrap it in a VecMonitor wrapper which will also allow to keep track of the agent progress.

from procgen import ProcgenEnv

from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import VecExtractDictObs, VecMonitor

# ProcgenEnv is already vectorized
venv = ProcgenEnv(num_envs=2, env_name="starpilot")

# To use only part of the observation:
# venv = VecExtractDictObs(venv, "rgb")

# Wrap with a VecMonitor to collect stats and avoid errors
venv = VecMonitor(venv=venv)

model = PPO("MultiInputPolicy", venv, verbose=1)

SB3 with EnvPool or Isaac Gym

Just like Procgen (see above), EnvPool and Isaac Gym accelerate the environment by already providing a vectorized implementation.

To use SB3 with those tools, you must wrap the env with tool’s specific VecEnvWrapper that will pre-process the data for SB3, you can find links to those wrappers in issue #772.

Record a Video

Record a mp4 video (here using a random agent).


It requires ffmpeg or avconv to be installed on the machine.

import gymnasium as gym
from stable_baselines3.common.vec_env import VecVideoRecorder, DummyVecEnv

env_id = "CartPole-v1"
video_folder = "logs/videos/"
video_length = 100

vec_env = DummyVecEnv([lambda: gym.make(env_id, render_mode="rgb_array")])

obs = vec_env.reset()

# Record the video starting at the first step
vec_env = VecVideoRecorder(vec_env, video_folder,
                       record_video_trigger=lambda x: x == 0, video_length=video_length,

for _ in range(video_length + 1):
  action = [vec_env.action_space.sample()]
  obs, _, _, _ = vec_env.step(action)
# Save the video

Bonus: Make a GIF of a Trained Agent

import imageio
import numpy as np

from stable_baselines3 import A2C

model = A2C("MlpPolicy", "LunarLander-v2").learn(100_000)

images = []
obs = model.env.reset()
img = model.env.render(mode="rgb_array")
for i in range(350):
    action, _ = model.predict(obs)
    obs, _, _ ,_ = model.env.step(action)
    img = model.env.render(mode="rgb_array")

imageio.mimsave("lander_a2c.gif", [np.array(img) for i, img in enumerate(images) if i%2 == 0], fps=29)