SAC

Soft Actor Critic (SAC) Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.

SAC is the successor of Soft Q-Learning SQL and incorporates the double Q-learning trick from TD3. A key feature of SAC, and a major difference with common RL algorithms, is that it is trained to maximize a trade-off between expected return and entropy, a measure of randomness in the policy.

Warning

The SAC model does not support stable_baselines.common.policies because it uses double q-values and value estimation, as a result it must use its own policy models (see SAC Policies).

Available Policies

MlpPolicy Policy object that implements actor critic, using a MLP (2 layers of 64)
LnMlpPolicy Policy object that implements actor critic, using a MLP (2 layers of 64), with layer normalisation
CnnPolicy Policy object that implements actor critic, using a CNN (the nature CNN)
LnCnnPolicy Policy object that implements actor critic, using a CNN (the nature CNN), with layer normalisation

Notes

Note

In our implementation, we use an entropy coefficient (as in OpenAI Spinning or Facebook Horizon), which is the equivalent to the inverse of reward scale in the original SAC paper. The main reason is that it avoids having too high errors when updating the Q functions.

Note

The default policies for SAC differ a bit from others MlpPolicy: it uses ReLU instead of tanh activation, to match the original paper

Can I use?

  • Recurrent policies: ❌
  • Multi processing: ❌
  • Gym spaces:
Space Action Observation
Discrete ✔️
Box ✔️ ✔️
MultiDiscrete ✔️
MultiBinary ✔️

Example

import gym
import numpy as np

from stable_baselines.sac.policies import MlpPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import SAC

env = gym.make('Pendulum-v0')
env = DummyVecEnv([lambda: env])

model = SAC(MlpPolicy, env, verbose=1)
model.learn(total_timesteps=50000, log_interval=10)
model.save("sac_pendulum")

del model # remove to demonstrate saving and loading

model = SAC.load("sac_pendulum")

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

Parameters

class stable_baselines.sac.SAC(policy, env, gamma=0.99, learning_rate=0.0003, buffer_size=50000, learning_starts=100, train_freq=1, batch_size=64, tau=0.005, ent_coef='auto', target_update_interval=1, gradient_steps=1, target_entropy='auto', action_noise=None, random_exploration=0.0, verbose=0, tensorboard_log=None, _init_setup_model=True, policy_kwargs=None, full_tensorboard_log=False, seed=None, n_cpu_tf_sess=None)[source]

Soft Actor-Critic (SAC) Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor, This implementation borrows code from original implementation (https://github.com/haarnoja/sac) from OpenAI Spinning Up (https://github.com/openai/spinningup) and from the Softlearning repo (https://github.com/rail-berkeley/softlearning/) Paper: https://arxiv.org/abs/1801.01290 Introduction to SAC: https://spinningup.openai.com/en/latest/algorithms/sac.html

Parameters:
  • policy – (SACPolicy or str) The policy model to use (MlpPolicy, CnnPolicy, LnMlpPolicy, …)
  • env – (Gym environment or str) The environment to learn from (if registered in Gym, can be str)
  • gamma – (float) the discount factor
  • learning_rate – (float or callable) learning rate for adam optimizer, the same learning rate will be used for all networks (Q-Values, Actor and Value function) it can be a function of the current progress (from 1 to 0)
  • buffer_size – (int) size of the replay buffer
  • batch_size – (int) Minibatch size for each gradient update
  • tau – (float) the soft update coefficient (“polyak update”, between 0 and 1)
  • ent_coef – (str or float) Entropy regularization coefficient. (Equivalent to inverse of reward scale in the original SAC paper.) Controlling exploration/exploitation trade-off. Set it to ‘auto’ to learn it automatically (and ‘auto_0.1’ for using 0.1 as initial value)
  • train_freq – (int) Update the model every train_freq steps.
  • learning_starts – (int) how many steps of the model to collect transitions for before learning starts
  • target_update_interval – (int) update the target network every target_network_update_freq steps.
  • gradient_steps – (int) How many gradient update after each step
  • target_entropy – (str or float) target entropy when learning ent_coef (ent_coef = ‘auto’)
  • action_noise – (ActionNoise) the action noise type (None by default), this can help for hard exploration problem. Cf DDPG for the different action noise type.
  • random_exploration – (float) Probability of taking a random action (as in an epsilon-greedy strategy) This is not needed for SAC normally but can help exploring when using HER + SAC. This hack was present in the original OpenAI Baselines repo (DDPG + HER)
  • verbose – (int) the verbosity level: 0 none, 1 training information, 2 tensorflow debug
  • tensorboard_log – (str) the log location for tensorboard (if None, no logging)
  • _init_setup_model – (bool) Whether or not to build the network at the creation of the instance
  • policy_kwargs – (dict) additional arguments to be passed to the policy on creation
  • full_tensorboard_log – (bool) enable additional logging when using tensorboard Note: this has no effect on SAC logging for now
  • seed – (int) Seed for the pseudo-random generators (python, numpy, tensorflow). If None (default), use random seed. Note that if you want completely deterministic results, you must set n_cpu_tf_sess to 1.
  • n_cpu_tf_sess – (int) The number of threads for TensorFlow operations If None, the number of cpu of the current machine will be used.
action_probability(observation, state=None, mask=None, actions=None, logp=False)[source]

If actions is None, then get the model’s action probability distribution from a given observation.

Depending on the action space the output is:
  • Discrete: probability for each possible action
  • Box: mean and standard deviation of the action output

However if actions is not None, this function will return the probability that the given actions are taken with the given parameters (observation, state, …) on this model. For discrete action spaces, it returns the probability mass; for continuous action spaces, the probability density. This is since the probability mass will always be zero in continuous spaces, see http://blog.christianperone.com/2019/01/ for a good explanation

Parameters:
  • observation – (np.ndarray) the input observation
  • state – (np.ndarray) The last states (can be None, used in recurrent policies)
  • mask – (np.ndarray) The last masks (can be None, used in recurrent policies)
  • actions – (np.ndarray) (OPTIONAL) For calculating the likelihood that the given actions are chosen by the model for each of the given parameters. Must have the same number of actions and observations. (set to None to return the complete action probability distribution)
  • logp – (bool) (OPTIONAL) When specified with actions, returns probability in log-space. This has no effect if actions is None.
Returns:

(np.ndarray) the model’s (log) action probability

get_env()

returns the current environment (can be None if not defined)

Returns:(Gym Environment) The current environment
get_parameter_list()[source]

Get tensorflow Variables of model’s parameters

This includes all variables necessary for continuing training (saving / loading).

Returns:(list) List of tensorflow Variables
get_parameters()

Get current model parameters as dictionary of variable name -> ndarray.

Returns:(OrderedDict) Dictionary of variable name -> ndarray of model’s parameters.
learn(total_timesteps, callback=None, log_interval=4, tb_log_name='SAC', reset_num_timesteps=True, replay_wrapper=None)[source]

Return a trained model.

Parameters:
  • total_timesteps – (int) The total number of samples to train on
  • callback – (function (dict, dict)) -> boolean function called at every steps with state of the algorithm. It takes the local and global variables. If it returns False, training is aborted.
  • log_interval – (int) The number of timesteps before logging.
  • tb_log_name – (str) the name of the run for tensorboard log
  • reset_num_timesteps – (bool) whether or not to reset the current timestep number (used in logging)
Returns:

(BaseRLModel) the trained model

classmethod load(load_path, env=None, custom_objects=None, **kwargs)

Load the model from file

Parameters:
  • load_path – (str or file-like) the saved parameter location
  • env – (Gym Envrionment) the new environment to run the loaded model on (can be None if you only need prediction from a trained model)
  • custom_objects – (dict) 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.
  • kwargs – extra arguments to change the model when loading
load_parameters(load_path_or_dict, exact_match=True)

Load model parameters from a file or a dictionary

Dictionary keys should be tensorflow variable names, which can be obtained with get_parameters function. If exact_match is True, dictionary should contain keys for all model’s parameters, otherwise RunTimeError is raised. If False, only variables included in the dictionary will be updated.

This does not load agent’s hyper-parameters.

Warning

This function does not update trainer/optimizer variables (e.g. momentum). As such training after using this function may lead to less-than-optimal results.

Parameters:
  • load_path_or_dict – (str or file-like or dict) Save parameter location or dict of parameters as variable.name -> ndarrays to be loaded.
  • exact_match – (bool) If True, expects load dictionary to contain keys for all variables in the model. If False, loads parameters only for variables mentioned in the dictionary. Defaults to True.
predict(observation, state=None, mask=None, deterministic=True)[source]

Get the model’s action from an observation

Parameters:
  • observation – (np.ndarray) the input observation
  • state – (np.ndarray) The last states (can be None, used in recurrent policies)
  • mask – (np.ndarray) The last masks (can be None, used in recurrent policies)
  • deterministic – (bool) Whether or not to return deterministic actions.
Returns:

(np.ndarray, np.ndarray) the model’s action and the next state (used in recurrent policies)

pretrain(dataset, n_epochs=10, learning_rate=0.0001, adam_epsilon=1e-08, val_interval=None)

Pretrain a model using behavior cloning: supervised learning given an expert dataset.

NOTE: only Box and Discrete spaces are supported for now.

Parameters:
  • dataset – (ExpertDataset) Dataset manager
  • n_epochs – (int) Number of iterations on the training set
  • learning_rate – (float) Learning rate
  • adam_epsilon – (float) the epsilon value for the adam optimizer
  • val_interval – (int) Report training and validation losses every n epochs. By default, every 10th of the maximum number of epochs.
Returns:

(BaseRLModel) the pretrained model

save(save_path, cloudpickle=False)[source]

Save the current parameters to file

Parameters:
  • save_path – (str or file-like) The save location
  • cloudpickle – (bool) Use older cloudpickle format instead of zip-archives.
set_env(env)

Checks the validity of the environment, and if it is coherent, set it as the current environment.

Parameters:env – (Gym Environment) The environment for learning a policy
set_random_seed(seed)
Parameters:seed – (int) Seed for the pseudo-random generators. If None, do not change the seeds.
setup_model()[source]

Create all the functions and tensorflow graphs necessary to train the model

SAC Policies

class stable_baselines.sac.MlpPolicy(sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs)[source]

Policy object that implements actor critic, using a MLP (2 layers of 64)

Parameters:
  • sess – (TensorFlow session) The current TensorFlow session
  • ob_space – (Gym Space) The observation space of the environment
  • ac_space – (Gym Space) The action space of the environment
  • n_env – (int) The number of environments to run
  • n_steps – (int) The number of steps to run for each environment
  • n_batch – (int) The number of batch to run (n_envs * n_steps)
  • reuse – (bool) If the policy is reusable or not
  • _kwargs – (dict) Extra keyword arguments for the nature CNN feature extraction
action_ph

tf.Tensor: placeholder for actions, shape (self.n_batch, ) + self.ac_space.shape.

initial_state

The initial state of the policy. For feedforward policies, None. For a recurrent policy, a NumPy array of shape (self.n_env, ) + state_shape.

is_discrete

bool: is action space discrete.

make_actor(obs=None, reuse=False, scope='pi')

Creates an actor object

Parameters:
  • obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder)
  • reuse – (bool) whether or not to resue parameters
  • scope – (str) the scope name of the actor
Returns:

(TensorFlow Tensor) the output tensor

make_critics(obs=None, action=None, reuse=False, scope='values_fn', create_vf=True, create_qf=True)

Creates the two Q-Values approximator along with the Value function

Parameters:
  • obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder)
  • action – (TensorFlow Tensor) The action placeholder
  • reuse – (bool) whether or not to resue parameters
  • scope – (str) the scope name
  • create_vf – (bool) Whether to create Value fn or not
  • create_qf – (bool) Whether to create Q-Values fn or not
Returns:

([tf.Tensor]) Mean, action and log probability

obs_ph

tf.Tensor: placeholder for observations, shape (self.n_batch, ) + self.ob_space.shape.

proba_step(obs, state=None, mask=None)

Returns the action probability params (mean, std) for a single step

Parameters:
  • obs – ([float] or [int]) The current observation of the environment
  • state – ([float]) The last states (used in recurrent policies)
  • mask – ([float]) The last masks (used in recurrent policies)
Returns:

([float], [float])

processed_obs

tf.Tensor: processed observations, shape (self.n_batch, ) + self.ob_space.shape.

The form of processing depends on the type of the observation space, and the parameters whether scale is passed to the constructor; see observation_input for more information.

step(obs, state=None, mask=None, deterministic=False)

Returns the policy for a single step

Parameters:
  • obs – ([float] or [int]) The current observation of the environment
  • state – ([float]) The last states (used in recurrent policies)
  • mask – ([float]) The last masks (used in recurrent policies)
  • deterministic – (bool) Whether or not to return deterministic actions.
Returns:

([float]) actions

class stable_baselines.sac.LnMlpPolicy(sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs)[source]

Policy object that implements actor critic, using a MLP (2 layers of 64), with layer normalisation

Parameters:
  • sess – (TensorFlow session) The current TensorFlow session
  • ob_space – (Gym Space) The observation space of the environment
  • ac_space – (Gym Space) The action space of the environment
  • n_env – (int) The number of environments to run
  • n_steps – (int) The number of steps to run for each environment
  • n_batch – (int) The number of batch to run (n_envs * n_steps)
  • reuse – (bool) If the policy is reusable or not
  • _kwargs – (dict) Extra keyword arguments for the nature CNN feature extraction
action_ph

tf.Tensor: placeholder for actions, shape (self.n_batch, ) + self.ac_space.shape.

initial_state

The initial state of the policy. For feedforward policies, None. For a recurrent policy, a NumPy array of shape (self.n_env, ) + state_shape.

is_discrete

bool: is action space discrete.

make_actor(obs=None, reuse=False, scope='pi')

Creates an actor object

Parameters:
  • obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder)
  • reuse – (bool) whether or not to resue parameters
  • scope – (str) the scope name of the actor
Returns:

(TensorFlow Tensor) the output tensor

make_critics(obs=None, action=None, reuse=False, scope='values_fn', create_vf=True, create_qf=True)

Creates the two Q-Values approximator along with the Value function

Parameters:
  • obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder)
  • action – (TensorFlow Tensor) The action placeholder
  • reuse – (bool) whether or not to resue parameters
  • scope – (str) the scope name
  • create_vf – (bool) Whether to create Value fn or not
  • create_qf – (bool) Whether to create Q-Values fn or not
Returns:

([tf.Tensor]) Mean, action and log probability

obs_ph

tf.Tensor: placeholder for observations, shape (self.n_batch, ) + self.ob_space.shape.

proba_step(obs, state=None, mask=None)

Returns the action probability params (mean, std) for a single step

Parameters:
  • obs – ([float] or [int]) The current observation of the environment
  • state – ([float]) The last states (used in recurrent policies)
  • mask – ([float]) The last masks (used in recurrent policies)
Returns:

([float], [float])

processed_obs

tf.Tensor: processed observations, shape (self.n_batch, ) + self.ob_space.shape.

The form of processing depends on the type of the observation space, and the parameters whether scale is passed to the constructor; see observation_input for more information.

step(obs, state=None, mask=None, deterministic=False)

Returns the policy for a single step

Parameters:
  • obs – ([float] or [int]) The current observation of the environment
  • state – ([float]) The last states (used in recurrent policies)
  • mask – ([float]) The last masks (used in recurrent policies)
  • deterministic – (bool) Whether or not to return deterministic actions.
Returns:

([float]) actions

class stable_baselines.sac.CnnPolicy(sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs)[source]

Policy object that implements actor critic, using a CNN (the nature CNN)

Parameters:
  • sess – (TensorFlow session) The current TensorFlow session
  • ob_space – (Gym Space) The observation space of the environment
  • ac_space – (Gym Space) The action space of the environment
  • n_env – (int) The number of environments to run
  • n_steps – (int) The number of steps to run for each environment
  • n_batch – (int) The number of batch to run (n_envs * n_steps)
  • reuse – (bool) If the policy is reusable or not
  • _kwargs – (dict) Extra keyword arguments for the nature CNN feature extraction
action_ph

tf.Tensor: placeholder for actions, shape (self.n_batch, ) + self.ac_space.shape.

initial_state

The initial state of the policy. For feedforward policies, None. For a recurrent policy, a NumPy array of shape (self.n_env, ) + state_shape.

is_discrete

bool: is action space discrete.

make_actor(obs=None, reuse=False, scope='pi')

Creates an actor object

Parameters:
  • obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder)
  • reuse – (bool) whether or not to resue parameters
  • scope – (str) the scope name of the actor
Returns:

(TensorFlow Tensor) the output tensor

make_critics(obs=None, action=None, reuse=False, scope='values_fn', create_vf=True, create_qf=True)

Creates the two Q-Values approximator along with the Value function

Parameters:
  • obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder)
  • action – (TensorFlow Tensor) The action placeholder
  • reuse – (bool) whether or not to resue parameters
  • scope – (str) the scope name
  • create_vf – (bool) Whether to create Value fn or not
  • create_qf – (bool) Whether to create Q-Values fn or not
Returns:

([tf.Tensor]) Mean, action and log probability

obs_ph

tf.Tensor: placeholder for observations, shape (self.n_batch, ) + self.ob_space.shape.

proba_step(obs, state=None, mask=None)

Returns the action probability params (mean, std) for a single step

Parameters:
  • obs – ([float] or [int]) The current observation of the environment
  • state – ([float]) The last states (used in recurrent policies)
  • mask – ([float]) The last masks (used in recurrent policies)
Returns:

([float], [float])

processed_obs

tf.Tensor: processed observations, shape (self.n_batch, ) + self.ob_space.shape.

The form of processing depends on the type of the observation space, and the parameters whether scale is passed to the constructor; see observation_input for more information.

step(obs, state=None, mask=None, deterministic=False)

Returns the policy for a single step

Parameters:
  • obs – ([float] or [int]) The current observation of the environment
  • state – ([float]) The last states (used in recurrent policies)
  • mask – ([float]) The last masks (used in recurrent policies)
  • deterministic – (bool) Whether or not to return deterministic actions.
Returns:

([float]) actions

class stable_baselines.sac.LnCnnPolicy(sess, ob_space, ac_space, n_env=1, n_steps=1, n_batch=None, reuse=False, **_kwargs)[source]

Policy object that implements actor critic, using a CNN (the nature CNN), with layer normalisation

Parameters:
  • sess – (TensorFlow session) The current TensorFlow session
  • ob_space – (Gym Space) The observation space of the environment
  • ac_space – (Gym Space) The action space of the environment
  • n_env – (int) The number of environments to run
  • n_steps – (int) The number of steps to run for each environment
  • n_batch – (int) The number of batch to run (n_envs * n_steps)
  • reuse – (bool) If the policy is reusable or not
  • _kwargs – (dict) Extra keyword arguments for the nature CNN feature extraction
action_ph

tf.Tensor: placeholder for actions, shape (self.n_batch, ) + self.ac_space.shape.

initial_state

The initial state of the policy. For feedforward policies, None. For a recurrent policy, a NumPy array of shape (self.n_env, ) + state_shape.

is_discrete

bool: is action space discrete.

make_actor(obs=None, reuse=False, scope='pi')

Creates an actor object

Parameters:
  • obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder)
  • reuse – (bool) whether or not to resue parameters
  • scope – (str) the scope name of the actor
Returns:

(TensorFlow Tensor) the output tensor

make_critics(obs=None, action=None, reuse=False, scope='values_fn', create_vf=True, create_qf=True)

Creates the two Q-Values approximator along with the Value function

Parameters:
  • obs – (TensorFlow Tensor) The observation placeholder (can be None for default placeholder)
  • action – (TensorFlow Tensor) The action placeholder
  • reuse – (bool) whether or not to resue parameters
  • scope – (str) the scope name
  • create_vf – (bool) Whether to create Value fn or not
  • create_qf – (bool) Whether to create Q-Values fn or not
Returns:

([tf.Tensor]) Mean, action and log probability

obs_ph

tf.Tensor: placeholder for observations, shape (self.n_batch, ) + self.ob_space.shape.

proba_step(obs, state=None, mask=None)

Returns the action probability params (mean, std) for a single step

Parameters:
  • obs – ([float] or [int]) The current observation of the environment
  • state – ([float]) The last states (used in recurrent policies)
  • mask – ([float]) The last masks (used in recurrent policies)
Returns:

([float], [float])

processed_obs

tf.Tensor: processed observations, shape (self.n_batch, ) + self.ob_space.shape.

The form of processing depends on the type of the observation space, and the parameters whether scale is passed to the constructor; see observation_input for more information.

step(obs, state=None, mask=None, deterministic=False)

Returns the policy for a single step

Parameters:
  • obs – ([float] or [int]) The current observation of the environment
  • state – ([float]) The last states (used in recurrent policies)
  • mask – ([float]) The last masks (used in recurrent policies)
  • deterministic – (bool) Whether or not to return deterministic actions.
Returns:

([float]) actions

Custom Policy Network

Similarly to the example given in the examples page. You can easily define a custom architecture for the policy network:

import gym

from stable_baselines.sac.policies import FeedForwardPolicy
from stable_baselines.common.vec_env import DummyVecEnv
from stable_baselines import SAC

# Custom MLP policy of three layers of size 128 each
class CustomSACPolicy(FeedForwardPolicy):
    def __init__(self, *args, **kwargs):
        super(CustomSACPolicy, self).__init__(*args, **kwargs,
                                           layers=[128, 128, 128],
                                           layer_norm=False,
                                           feature_extraction="mlp")

# Create and wrap the environment
env = gym.make('Pendulum-v0')
env = DummyVecEnv([lambda: env])

model = SAC(CustomSACPolicy, env, verbose=1)
# Train the agent
model.learn(total_timesteps=100000)