A2C

A synchronous, deterministic variant of Asynchronous Advantage Actor Critic (A3C). It uses multiple workers to avoid the use of a replay buffer.

Warning

If you find training unstable or want to match performance of stable-baselines A2C, consider using RMSpropTFLike optimizer from stable_baselines3.common.sb2_compat.rmsprop_tf_like. You can change optimizer with A2C(policy_kwargs=dict(optimizer_class=RMSpropTFLike, optimizer_kwargs=dict(eps=1e-5))). Read more here.

Notes

Can I use?

  • Recurrent policies: ❌

  • Multi processing: ✔️

  • Gym spaces:

Space

Action

Observation

Discrete

✔️

✔️

Box

✔️

✔️

MultiDiscrete

✔️

✔️

MultiBinary

✔️

✔️

Dict

❌

✔️

Example

This example is 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 RL Zoo repository.

Train a A2C agent on CartPole-v1 using 4 environments.

from stable_baselines3 import A2C
from stable_baselines3.common.env_util import make_vec_env

# Parallel environments
vec_env = make_vec_env("CartPole-v1", n_envs=4)

model = A2C("MlpPolicy", vec_env, verbose=1)
model.learn(total_timesteps=25000)
model.save("a2c_cartpole")

del model # remove to demonstrate saving and loading

model = A2C.load("a2c_cartpole")

obs = vec_env.reset()
while True:
    action, _states = model.predict(obs)
    obs, rewards, dones, info = vec_env.step(action)
    vec_env.render("human")

Note

A2C is meant to be run primarily on the CPU, especially when you are not using a CNN. To improve CPU utilization, try turning off the GPU and using SubprocVecEnv instead of the default DummyVecEnv:

from stable_baselines3 import A2C
from stable_baselines3.common.env_util import make_vec_env
from stable_baselines3.common.vec_env import SubprocVecEnv

if __name__=="__main__":
    env = make_vec_env("CartPole-v1", n_envs=8, vec_env_cls=SubprocVecEnv)
    model = A2C("MlpPolicy", env, device="cpu")
    model.learn(total_timesteps=25_000)

For more information, see Vectorized Environments, Issue #1245 or the Multiprocessing notebook.

Results

Atari Games

The complete learning curves are available in the associated PR #110.

PyBullet Environments

Results on the PyBullet benchmark (2M steps) using 6 seeds. The complete learning curves are available in the associated issue #48.

Note

Hyperparameters from the gSDE paper were used (as they are tuned for PyBullet envs).

Gaussian means that the unstructured Gaussian noise is used for exploration, gSDE (generalized State-Dependent Exploration) is used otherwise.

Environments

A2C

A2C

PPO

PPO

Gaussian

gSDE

Gaussian

gSDE

HalfCheetah

2003 +/- 54

2032 +/- 122

1976 +/- 479

2826 +/- 45

Ant

2286 +/- 72

2443 +/- 89

2364 +/- 120

2782 +/- 76

Hopper

1627 +/- 158

1561 +/- 220

1567 +/- 339

2512 +/- 21

Walker2D

577 +/- 65

839 +/- 56

1230 +/- 147

2019 +/- 64

How to replicate the results?

Clone the rl-zoo repo:

git clone https://github.com/DLR-RM/rl-baselines3-zoo
cd rl-baselines3-zoo/

Run the benchmark (replace $ENV_ID by the envs mentioned above):

python train.py --algo a2c --env $ENV_ID --eval-episodes 10 --eval-freq 10000

Plot the results (here for PyBullet envs only):

python scripts/all_plots.py -a a2c -e HalfCheetah Ant Hopper Walker2D -f logs/ -o logs/a2c_results
python scripts/plot_from_file.py -i logs/a2c_results.pkl -latex -l A2C

Parameters

class stable_baselines3.a2c.A2C(policy, env, learning_rate=0.0007, n_steps=5, gamma=0.99, gae_lambda=1.0, ent_coef=0.0, vf_coef=0.5, max_grad_norm=0.5, rms_prop_eps=1e-05, use_rms_prop=True, use_sde=False, sde_sample_freq=-1, normalize_advantage=False, stats_window_size=100, tensorboard_log=None, policy_kwargs=None, verbose=0, seed=None, device='auto', _init_setup_model=True)[source]

Advantage Actor Critic (A2C)

Paper: https://arxiv.org/abs/1602.01783 Code: This implementation borrows code from https://github.com/ikostrikov/pytorch-a2c-ppo-acktr-gail and and Stable Baselines (https://github.com/hill-a/stable-baselines)

Introduction to A2C: https://hackernoon.com/intuitive-rl-intro-to-advantage-actor-critic-a2c-4ff545978752

Parameters:

  • policy (Union[str, Type[ActorCriticPolicy]]) – The policy model to use (MlpPolicy, CnnPolicy, …)

  • env (Union[Env, VecEnv, str]) – The environment to learn from (if registered in Gym, can be str)

  • learning_rate (Union[float, Callable[[float], float]]) – The learning rate, it can be a function of the current progress remaining (from 1 to 0)

  • n_steps (int) – The number of steps to run for each environment per update (i.e. batch size is n_steps * n_env where n_env is number of environment copies running in parallel)

  • gamma (float) – Discount factor

  • gae_lambda (float) – Factor for trade-off of bias vs variance for Generalized Advantage Estimator. Equivalent to classic advantage when set to 1.

  • ent_coef (float) – Entropy coefficient for the loss calculation

  • vf_coef (float) – Value function coefficient for the loss calculation

  • max_grad_norm (float) – The maximum value for the gradient clipping

  • rms_prop_eps (float) – RMSProp epsilon. It stabilizes square root computation in denominator of RMSProp update

  • use_rms_prop (bool) – Whether to use RMSprop (default) or Adam as optimizer

  • use_sde (bool) – Whether to use generalized State Dependent Exploration (gSDE) instead of action noise exploration (default: False)

  • sde_sample_freq (int) – Sample a new noise matrix every n steps when using gSDE Default: -1 (only sample at the beginning of the rollout)

  • normalize_advantage (bool) – Whether to normalize or not the advantage

  • stats_window_size (int) – Window size for the rollout logging, specifying the number of episodes to average the reported success rate, mean episode length, and mean reward over

  • tensorboard_log (Optional[str]) – the log location for tensorboard (if None, no logging)

  • policy_kwargs (Optional[Dict[str, Any]]) – additional arguments to be passed to the policy on creation

  • verbose (int) – Verbosity level: 0 for no output, 1 for info messages (such as device or wrappers used), 2 for debug messages

  • seed (Optional[int]) – Seed for the pseudo random generators

  • device (Union[device, str]) – Device (cpu, cuda, …) on which the code should be run. Setting it to auto, the code will be run on the GPU if possible.

  • _init_setup_model (bool) – Whether or not to build the network at the creation of the instance

collect_rollouts(env, callback, rollout_buffer, n_rollout_steps)

Collect experiences using the current policy and fill a RolloutBuffer. The term rollout here refers to the model-free notion and should not be used with the concept of rollout used in model-based RL or planning.

Parameters:

  • env (VecEnv) – The training environment

  • callback (BaseCallback) – Callback that will be called at each step (and at the beginning and end of the rollout)

  • rollout_buffer (RolloutBuffer) – Buffer to fill with rollouts

  • n_rollout_steps (int) – Number of experiences to collect per environment

Return type:

bool

Returns:

True if function returned with at least n_rollout_steps collected, False if callback terminated rollout prematurely.

get_env()

Returns the current environment (can be None if not defined).

Return type:

Optional[VecEnv]

Returns:

The current environment

get_parameters()

Return the parameters of the agent. This includes parameters from different networks, e.g. critics (value functions) and policies (pi functions).

Return type:

Dict[str, Dict]

Returns:

Mapping of from names of the objects to PyTorch state-dicts.

get_vec_normalize_env()

Return the VecNormalize wrapper of the training env if it exists.

Return type:

Optional[VecNormalize]

Returns:

The VecNormalize env.

learn(total_timesteps, callback=None, log_interval=100, tb_log_name='A2C', reset_num_timesteps=True, progress_bar=False)[source]

Return a trained model.

Parameters:

  • total_timesteps (int) – The total number of samples (env steps) to train on

  • callback (Union[None, Callable, List[BaseCallback], BaseCallback]) – callback(s) called at every step with state of the algorithm.

  • log_interval (int) – The number of episodes before logging.

  • tb_log_name (str) – the name of the run for TensorBoard logging

  • reset_num_timesteps (bool) – whether or not to reset the current timestep number (used in logging)

  • progress_bar (bool) – Display a progress bar using tqdm and rich.

Return type:

TypeVar(SelfA2C, bound= A2C)

Returns:

the trained model

classmethod load(path, env=None, device='auto', custom_objects=None, print_system_info=False, force_reset=True, **kwargs)

Load the model from a zip-file. Warning: load re-creates the model from scratch, it does not update it in-place! For an in-place load use set_parameters instead.

Parameters:

  • path (Union[str, Path, BufferedIOBase]) – path to the file (or a file-like) where to load the agent from

  • env (Union[Env, VecEnv, None]) – the new environment to run the loaded model on (can be None if you only need prediction from a trained model) has priority over any saved environment

  • device (Union[device, str]) – Device on which the code should run.

  • custom_objects (Optional[Dict[str, Any]]) – Dictionary of objects to replace upon loading. If a variable is present in this dictionary as a key, it will not be deserialized and the corresponding item will be used instead. Similar to custom_objects in keras.models.load_model. Useful when you have an object in file that can not be deserialized.

  • print_system_info (bool) – Whether to print system info from the saved model and the current system info (useful to debug loading issues)

  • force_reset (bool) – Force call to reset() before training to avoid unexpected behavior. See https://github.com/DLR-RM/stable-baselines3/issues/597

  • kwargs – extra arguments to change the model when loading

Return type:

TypeVar(SelfBaseAlgorithm, bound= BaseAlgorithm)

Returns:

new model instance with loaded parameters

property logger: Logger

Getter for the logger object.

predict(observation, state=None, episode_start=None, deterministic=False)

Get the policy action from an observation (and optional hidden state). Includes sugar-coating to handle different observations (e.g. normalizing images).

Parameters:

  • observation (Union[ndarray, Dict[str, ndarray]]) – the input observation

  • state (Optional[Tuple[ndarray, ...]]) – The last hidden states (can be None, used in recurrent policies)

  • episode_start (Optional[ndarray]) – The last masks (can be None, used in recurrent policies) this correspond to beginning of episodes, where the hidden states of the RNN must be reset.

  • deterministic (bool) – Whether or not to return deterministic actions.

Return type:

Tuple[ndarray, Optional[Tuple[ndarray, ...]]]

Returns:

the model’s action and the next hidden state (used in recurrent policies)

save(path, exclude=None, include=None)

Save all the attributes of the object and the model parameters in a zip-file.

Parameters:

  • path (Union[str, Path, BufferedIOBase]) – path to the file where the rl agent should be saved

  • exclude (Optional[Iterable[str]]) – name of parameters that should be excluded in addition to the default ones

  • include (Optional[Iterable[str]]) – name of parameters that might be excluded but should be included anyway

Return type:

None

set_env(env, force_reset=True)

Checks the validity of the environment, and if it is coherent, set it as the current environment. Furthermore wrap any non vectorized env into a vectorized checked parameters: - observation_space - action_space

Parameters:
Return type:

None

set_logger(logger)

Setter for for logger object. :rtype: None

Warning

When passing a custom logger object, this will overwrite tensorboard_log and verbose settings passed to the constructor.

set_parameters(load_path_or_dict, exact_match=True, device='auto')

Load parameters from a given zip-file or a nested dictionary containing parameters for different modules (see get_parameters).

Parameters:

  • load_path_or_iter – Location of the saved data (path or file-like, see save), or a nested dictionary containing nn.Module parameters used by the policy. The dictionary maps object names to a state-dictionary returned by torch.nn.Module.state_dict().

  • exact_match (bool) – If True, the given parameters should include parameters for each module and each of their parameters, otherwise raises an Exception. If set to False, this can be used to update only specific parameters.

  • device (Union[device, str]) – Device on which the code should run.

Return type:

None

set_random_seed(seed=None)

Set the seed of the pseudo-random generators (python, numpy, pytorch, gym, action_space)

Parameters:

seed (Optional[int]) –

Return type:

None

train()[source]

Update policy using the currently gathered rollout buffer (one gradient step over whole data).

Return type:

None

A2C Policies

stable_baselines3.a2c.MlpPolicy

alias of ActorCriticPolicy

class stable_baselines3.common.policies.ActorCriticPolicy(observation_space, action_space, lr_schedule, net_arch=None, activation_fn=<class 'torch.nn.modules.activation.Tanh'>, ortho_init=True, use_sde=False, log_std_init=0.0, full_std=True, use_expln=False, squash_output=False, features_extractor_class=<class 'stable_baselines3.common.torch_layers.FlattenExtractor'>, features_extractor_kwargs=None, share_features_extractor=True, normalize_images=True, optimizer_class=<class 'torch.optim.adam.Adam'>, optimizer_kwargs=None)[source]

Policy class for actor-critic algorithms (has both policy and value prediction). Used by A2C, PPO and the likes.

Parameters:

  • observation_space (Space) – Observation space

  • action_space (Space) – Action space

  • lr_schedule (Callable[[float], float]) – Learning rate schedule (could be constant)

  • net_arch (Union[List[int], Dict[str, List[int]], None]) – The specification of the policy and value networks.

  • activation_fn (Type[Module]) – Activation function

  • ortho_init (bool) – Whether to use or not orthogonal initialization

  • use_sde (bool) – Whether to use State Dependent Exploration or not

  • log_std_init (float) – Initial value for the log standard deviation

  • full_std (bool) – Whether to use (n_features x n_actions) parameters for the std instead of only (n_features,) when using gSDE

  • use_expln (bool) – Use expln() function instead of exp() to ensure a positive standard deviation (cf paper). It allows to keep variance above zero and prevent it from growing too fast. In practice, exp() is usually enough.

  • squash_output (bool) – Whether to squash the output using a tanh function, this allows to ensure boundaries when using gSDE.

  • features_extractor_class (Type[BaseFeaturesExtractor]) – Features extractor to use.

  • features_extractor_kwargs (Optional[Dict[str, Any]]) – Keyword arguments to pass to the features extractor.

  • share_features_extractor (bool) – If True, the features extractor is shared between the policy and value networks.

  • normalize_images (bool) – Whether to normalize images or not, dividing by 255.0 (True by default)

  • optimizer_class (Type[Optimizer]) – The optimizer to use, th.optim.Adam by default

  • optimizer_kwargs (Optional[Dict[str, Any]]) – Additional keyword arguments, excluding the learning rate, to pass to the optimizer

evaluate_actions(obs, actions)[source]

Evaluate actions according to the current policy, given the observations.

Parameters:

  • obs (Tensor) – Observation

  • actions (Tensor) – Actions

Return type:

Tuple[Tensor, Tensor, Optional[Tensor]]

Returns:

estimated value, log likelihood of taking those actions and entropy of the action distribution.

extract_features(obs)[source]

Preprocess the observation if needed and extract features.

Parameters:

obs (Tensor) – Observation

Return type:

Union[Tensor, Tuple[Tensor, Tensor]]

Returns:

the output of the features extractor(s)

forward(obs, deterministic=False)[source]

Forward pass in all the networks (actor and critic)

Parameters:

  • obs (Tensor) – Observation

  • deterministic (bool) – Whether to sample or use deterministic actions

Return type:

Tuple[Tensor, Tensor, Tensor]

Returns:

action, value and log probability of the action

get_distribution(obs)[source]

Get the current policy distribution given the observations.

Parameters:

obs (Tensor) –

Return type:

Distribution

Returns:

the action distribution.

predict_values(obs)[source]

Get the estimated values according to the current policy given the observations.

Parameters:

obs (Tensor) – Observation

Return type:

Tensor

Returns:

the estimated values.

reset_noise(n_envs=1)[source]

Sample new weights for the exploration matrix.

Parameters:

n_envs (int) –

Return type:

None

stable_baselines3.a2c.CnnPolicy

alias of ActorCriticCnnPolicy

class stable_baselines3.common.policies.ActorCriticCnnPolicy(observation_space, action_space, lr_schedule, net_arch=None, activation_fn=<class 'torch.nn.modules.activation.Tanh'>, ortho_init=True, use_sde=False, log_std_init=0.0, full_std=True, use_expln=False, squash_output=False, features_extractor_class=<class 'stable_baselines3.common.torch_layers.NatureCNN'>, features_extractor_kwargs=None, share_features_extractor=True, normalize_images=True, optimizer_class=<class 'torch.optim.adam.Adam'>, optimizer_kwargs=None)[source]

CNN policy class for actor-critic algorithms (has both policy and value prediction). Used by A2C, PPO and the likes.

Parameters:

  • observation_space (Space) – Observation space

  • action_space (Space) – Action space

  • lr_schedule (Callable[[float], float]) – Learning rate schedule (could be constant)

  • net_arch (Union[List[int], Dict[str, List[int]], None]) – The specification of the policy and value networks.

  • activation_fn (Type[Module]) – Activation function

  • ortho_init (bool) – Whether to use or not orthogonal initialization

  • use_sde (bool) – Whether to use State Dependent Exploration or not

  • log_std_init (float) – Initial value for the log standard deviation

  • full_std (bool) – Whether to use (n_features x n_actions) parameters for the std instead of only (n_features,) when using gSDE

  • use_expln (bool) – Use expln() function instead of exp() to ensure a positive standard deviation (cf paper). It allows to keep variance above zero and prevent it from growing too fast. In practice, exp() is usually enough.

  • squash_output (bool) – Whether to squash the output using a tanh function, this allows to ensure boundaries when using gSDE.

  • features_extractor_class (Type[BaseFeaturesExtractor]) – Features extractor to use.

  • features_extractor_kwargs (Optional[Dict[str, Any]]) – Keyword arguments to pass to the features extractor.

  • share_features_extractor (bool) – If True, the features extractor is shared between the policy and value networks.

  • normalize_images (bool) – Whether to normalize images or not, dividing by 255.0 (True by default)

  • optimizer_class (Type[Optimizer]) – The optimizer to use, th.optim.Adam by default

  • optimizer_kwargs (Optional[Dict[str, Any]]) – Additional keyword arguments, excluding the learning rate, to pass to the optimizer

stable_baselines3.a2c.MultiInputPolicy

alias of MultiInputActorCriticPolicy

class stable_baselines3.common.policies.MultiInputActorCriticPolicy(observation_space, action_space, lr_schedule, net_arch=None, activation_fn=<class 'torch.nn.modules.activation.Tanh'>, ortho_init=True, use_sde=False, log_std_init=0.0, full_std=True, use_expln=False, squash_output=False, features_extractor_class=<class 'stable_baselines3.common.torch_layers.CombinedExtractor'>, features_extractor_kwargs=None, share_features_extractor=True, normalize_images=True, optimizer_class=<class 'torch.optim.adam.Adam'>, optimizer_kwargs=None)[source]

MultiInputActorClass policy class for actor-critic algorithms (has both policy and value prediction). Used by A2C, PPO and the likes.

Parameters:

  • observation_space (Dict) – Observation space (Tuple)

  • action_space (Space) – Action space

  • lr_schedule (Callable[[float], float]) – Learning rate schedule (could be constant)

  • net_arch (Union[List[int], Dict[str, List[int]], None]) – The specification of the policy and value networks.

  • activation_fn (Type[Module]) – Activation function

  • ortho_init (bool) – Whether to use or not orthogonal initialization

  • use_sde (bool) – Whether to use State Dependent Exploration or not

  • log_std_init (float) – Initial value for the log standard deviation

  • full_std (bool) – Whether to use (n_features x n_actions) parameters for the std instead of only (n_features,) when using gSDE

  • use_expln (bool) – Use expln() function instead of exp() to ensure a positive standard deviation (cf paper). It allows to keep variance above zero and prevent it from growing too fast. In practice, exp() is usually enough.

  • squash_output (bool) – Whether to squash the output using a tanh function, this allows to ensure boundaries when using gSDE.

  • features_extractor_class (Type[BaseFeaturesExtractor]) – Uses the CombinedExtractor

  • features_extractor_kwargs (Optional[Dict[str, Any]]) – Keyword arguments to pass to the features extractor.

  • share_features_extractor (bool) – If True, the features extractor is shared between the policy and value networks.

  • normalize_images (bool) – Whether to normalize images or not, dividing by 255.0 (True by default)

  • optimizer_class (Type[Optimizer]) – The optimizer to use, th.optim.Adam by default

  • optimizer_kwargs (Optional[Dict[str, Any]]) – Additional keyword arguments, excluding the learning rate, to pass to the optimizer