SAC¶
Soft Actor Critic (SAC) OffPolicy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor.
SAC is the successor of Soft QLearning SQL and incorporates the double Qlearning trick from TD3. A key feature of SAC, and a major difference with common RL algorithms, is that it is trained to maximize a tradeoff 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 qvalues
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¶
 Original paper: https://arxiv.org/abs/1801.01290
 OpenAI Spinning Guide for SAC: https://spinningup.openai.com/en/latest/algorithms/sac.html
 Original Implementation: https://github.com/haarnoja/sac
 Blog post on using SAC with real robots: https://bair.berkeley.edu/blog/2018/12/14/sac/
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('Pendulumv0')
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 ActorCritic (SAC) OffPolicy 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/railberkeley/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 (QValues, 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 tradeoff. 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 epsilongreedy 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 pseudorandom 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
isNone
, 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 notNone
, 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 explanationParameters:  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 logspace. 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 filelike) 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. Ifexact_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 hyperparameters.
Warning
This function does not update trainer/optimizer variables (e.g. momentum). As such training after using this function may lead to lessthanoptimal results.
Parameters:  load_path_or_dict – (str or filelike 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=1e08, 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 filelike) The save location
 cloudpickle – (bool) Use older cloudpickle format instead of ziparchives.

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 pseudorandom generators. If None, do not change the seeds.
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 QValues 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 QValues 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 QValues 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 QValues 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 QValues 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 QValues 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 QValues 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 QValues 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('Pendulumv0')
env = DummyVecEnv([lambda: env])
model = SAC(CustomSACPolicy, env, verbose=1)
# Train the agent
model.learn(total_timesteps=100000)