rlSACAgent

Soft actor-critic reinforcement learning agent

Description

The soft actor-critic (SAC) algorithm is a model-free, online, off-policy, actor-critic reinforcement learning method. The SAC algorithm computes an optimal policy that maximizes both the long-term expected reward and the entropy of the policy. The policy entropy is a measure of policy uncertainty given the state. A higher entropy value promotes more exploration. Maximizing both the reward and the entropy balances exploration and exploitation of the environment. The action space can only be continuous.

For more information, see Soft Actor-Critic Agents.

For more information on the different types of reinforcement learning agents, see Reinforcement Learning Agents.

Creation

Syntax

agent = rlSACAgent(observationInfo,actionInfo)

agent = rlSACAgent(observationInfo,actionInfo,initOptions)

agent = rlSACAgent(actor,critics)

agent = rlSACAgent(___,agentOptions)

Description

Create Agent from Observation and Action Specifications

agent = rlSACAgent(observationInfo,actionInfo) creates a SAC agent for an environment with the given observation and action specifications, using default initialization options. The actor and critics in the agent use default deep neural networks built using the observation specification observationInfo and action specification actionInfo. The ObservationInfo and ActionInfo properties of agent are set to the observationInfo and actionInfo input arguments, respectively.

agent = rlSACAgent(observationInfo,actionInfo,initOptions) creates a SAC agent with deep neural networks configured using the specified initialization options (initOptions).

Create Agent from Actor and Critic

agent = rlSACAgent(actor,critics) creates a SAC agent with the specified actor and critic networks and default agent options.

Specify Agent Options

agent = rlSACAgent(___,agentOptions) sets the AgentOptions property for any of the previous syntaxes.

Input Arguments

initOptions — Agent initialization options

rlAgentInitializationOptions object

Agent initialization options, specified as an rlAgentInitializationOptions object.

actor — Actor

rlContinuousGaussianActor object

Actor that implements the policy, specified as an rlContinuousGaussianActor function approximator object. For more information on creating actor approximators, see Create Policies and Value Functions.

critics — Critic

rlQValueFunction object | two-element row vector of rlQValueFunction objects

Critic, specified as one of the following:

• rlQValueFunction object — Create a SAC agent with a single Q-value function.

• Two-element row vector of rlQValueFunction objects — Create a SAC agent with two critic value functions. The two critic must be unique rlQValueFunction objects with the same observation and action specifications. The critics can either have different structures or the same structure but with different initial parameters.

For a SAC agent, each critic must be a single-output rlQValueFunction object that takes both the action and observations as inputs.

For more information on creating critics, see Create Policies and Value Functions.

Properties

ObservationInfo — Observation specifications

specification object | array of specification objects

Observation specifications, specified as a reinforcement learning specification object or an array of specification objects defining properties such as dimensions, data type, and names of the observation signals.

If you create the agent by specifying an actor and critic, the value of ObservationInfo matches the value specified in the actor and critic objects.

You can extract observationInfo from an existing environment or agent using getObservationInfo. You can also construct the specifications manually using rlFiniteSetSpec or rlNumericSpec.

ActionInfo — Action specification

rlNumericSpec object

Action specification for a continuous action space, specified as an rlNumericSpec object defining properties such as dimensions, data type and name of the action signals.

If you create the agent by specifying an actor and critic, the value of ActionInfo matches the value specified in the actor and critic objects.

You can extract actionInfo from an existing environment or agent using getActionInfo. You can also construct the specification manually using rlFiniteSetSpec or rlNumericSpec.

AgentOptions — Agent options

rlSACAgentOptions object

Agent options, specified as an rlSACAgentOptions object.

If you create a SAC agent with default actor and critic that use recurrent neural networks, the default value of AgentOptions.SequenceLength is 32.

ExperienceBuffer — Experience buffer

rlReplayMemory object

Experience buffer, specified as an rlReplayMemory object. During training the agent stores each of its experiences (S,A,R,S',D) in a buffer. Here:

• S is the current observation of the environment.

• A is the action taken by the agent.

• R is the reward for taking action A.

• S' is the next observation after taking action A.

• D is the is-done signal after taking action A.

UseExplorationPolicy — Option to use exploration policy

true (default) | false

Option to use exploration policy when selecting actions, specified as a one of the following logical values.

• true — Use the base agent exploration policy when selecting actions in sim and generatePolicyFunction. In this case, the agent selects its actions by sampling its probability distribution, the policy is therefore stochastic and the agent explores its observation space.

• false — Use the base agent greedy policy (the action with maximum likelihood) when selecting actions in sim and generatePolicyFunction. In this case, the simulated agent and generated policy behave deterministically.

Note

This option affects only simulation and deployment; it does not affect training.

SampleTime — Sample time of agent

positive scalar | -1

Sample time of agent, specified as a positive scalar or as -1. Setting this parameter to -1 allows for event-based simulations. The value of SampleTime matches the value specified in AgentOptions.

Within a Simulink^® environment, the RL Agent block in which the agent is specified to execute every SampleTime seconds of simulation time. If SampleTime is -1, the block inherits the sample time from its parent subsystem.

Within a MATLAB^® environment, the agent is executed every time the environment advances. In this case, SampleTime is the time interval between consecutive elements in the output experience returned by sim or train. If SampleTime is -1, the time interval between consecutive elements in the returned output experience reflects the timing of the event that triggers the agent execution.

Object Functions

train	Train reinforcement learning agents within a specified environment
sim	Simulate trained reinforcement learning agents within specified environment
getAction	Obtain action from agent, actor, or policy object given environment observations
getActor	Get actor from reinforcement learning agent
setActor	Set actor of reinforcement learning agent
getCritic	Get critic from reinforcement learning agent
setCritic	Set critic of reinforcement learning agent
generatePolicyFunction	Generate function that evaluates policy of an agent or policy object

Examples

Create SAC Agent from Observation and Action Specifications

Create environment and obtain observation and action specifications. For this example, load the environment used in the example Train DDPG Agent to Control Double Integrator System. The observation from the environment is a vector containing the position and velocity of a mass. The action is a scalar representing a force, applied to the mass, ranging continuously from -2 to 2 Newton.

env = rlPredefinedEnv("DoubleIntegrator-Continuous");
obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);

The agent creation function initializes the actor and critic networks randomly. Ensure reproducibility by fixing the seed of the random generator.

rng(0)

Create a SAC agent from the environment observation and action specifications.

agent = rlSACAgent(obsInfo,actInfo);

To check your agent, use getAction to return the action from a random observation.

getAction(agent,{rand(obsInfo(1).Dimension)})

ans = 1x1 cell array
    {[0.0546]}

You can now test and train the agent within the environment. You can also use getActor and getCritic to extract the actor and critic, respectively, and getModel to extract the approximator model (by default a deep neural network) from the actor or critic.

Create SAC Agent Using Initialization Options

Create an environment with a continuous action space and obtain its observation and action specifications. For this example, load the environment used in the example Train DDPG Agent to Control Double Integrator System. The observation from the environment is a vector containing the position and velocity of a mass. The action is a scalar representing a force, applied to the mass, ranging continuously from -2 to 2 Newton.

env = rlPredefinedEnv("DoubleIntegrator-Continuous");
obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);

Create an agent initialization option object, specifying that each hidden fully connected layer in the network must have 128 neurons.

initOpts = rlAgentInitializationOptions(NumHiddenUnit=128);

The agent creation function initializes the actor and critic networks randomly. Ensure reproducibility by fixing the seed of the random generator.

rng(0)

Create a SAC agent from the environment observation and action specifications using the initialization options.

agent = rlSACAgent(obsInfo,actInfo,initOpts);

Extract the deep neural network from the actor.

actorNet = getModel(getActor(agent));

Extract the deep neural networks from the two critics. Note that getModel(critics) only returns the first critic network.

critics = getCritic(agent);
criticNet1 = getModel(critics(1));
criticNet2 = getModel(critics(2));

Display the layers of the first critic network, and verify that each hidden fully connected layer has 128 neurons.

criticNet1.Layers

ans = 
  9x1 Layer array with layers:

     1   'concat'        Concatenation     Concatenation of 2 inputs along dimension 1
     2   'relu_body'     ReLU              ReLU
     3   'fc_body'       Fully Connected   128 fully connected layer
     4   'body_output'   ReLU              ReLU
     5   'input_1'       Feature Input     2 features
     6   'fc_1'          Fully Connected   128 fully connected layer
     7   'input_2'       Feature Input     1 features
     8   'fc_2'          Fully Connected   128 fully connected layer
     9   'output'        Fully Connected   1 fully connected layer

Plot the networks of the actor and of the second critic, and display the number of weights.

plot(layerGraph(actorNet))

summary(actorNet)

   Initialized: true

   Number of learnables: 17.1k

   Inputs:
      1   'input_1'   2 features

plot(layerGraph(criticNet2))

summary(criticNet2)

   Initialized: true

   Number of learnables: 33.6k

   Inputs:
      1   'input_1'   2 features
      2   'input_2'   1 features

To check your agent, use getAction to return the action from a random observation.

getAction(agent,{rand(obsInfo(1).Dimension)})

ans = 1x1 cell array
    {[-0.9867]}

You can now test and train the agent within the environment.

Create SAC Agent from Actor and Critics

Create an environment and obtain observation and action specifications. For this example, load the environment used in the example Train DDPG Agent to Control Double Integrator System. The observations from the environment is a vector containing the position and velocity of a mass. The action is a scalar representing a force, applied to the mass, ranging continuously from -2 to 2 Newton.

env = rlPredefinedEnv("DoubleIntegrator-Continuous");
obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);

A SAC agent uses two Q-value function critics. To approximate each Q-value function, use a neural network. The network for a single-output Q-value function critic must have two input layers, one for the observation and the other for the action, and return a scalar value representing the expected cumulative long-term reward following from the given observation and action.

Define each network path as an array of layer objects, and the dimensions of the observation and action spaces from the environment specification objects.

% Observation path
obsPath = [
    featureInputLayer(prod(obsInfo.Dimension),Name="obsPathIn")
    fullyConnectedLayer(32)
    reluLayer
    fullyConnectedLayer(16,Name="obsPathOut")
    ];

% Action path
actPath = [
    featureInputLayer(prod(actInfo.Dimension),Name="actPathIn")
    fullyConnectedLayer(32)
    reluLayer
    fullyConnectedLayer(16,Name="actPathOut")
    ];

% Common path
commonPath = [
    concatenationLayer(1,2,Name="concat")
    reluLayer
    fullyConnectedLayer(1)
    ];

% Add layers to layergraph object
criticNet = layerGraph;
criticNet = addLayers(criticNet,obsPath);
criticNet = addLayers(criticNet,actPath);
criticNet = addLayers(criticNet,commonPath);

% Connect layers
criticNet = connectLayers(criticNet,"obsPathOut","concat/in1");
criticNet = connectLayers(criticNet,"actPathOut","concat/in2");

To initialize the network weights differently for the two critics, create two different dlnetwork objects. You must do this because if the agent constructor function does not accept two identical critics.

criticNet1 = dlnetwork(criticNet);
criticNet2 = dlnetwork(criticNet);

Display the number of weights.

summary(criticNet1)

   Initialized: true

   Number of learnables: 1.2k

   Inputs:
      1   'obsPathIn'   2 features
      2   'actPathIn'   1 features

Create the two critics using rlQValueFunction, using the two networks with different weights. Alternatively, if you use exactly the same network with the same weights, you must explicitly initialize the network each time (to make sure weights are initialized differently) before passing it to rlQValueFunction. To do so, use initialize.

critic1 = rlQValueFunction(criticNet1,obsInfo,actInfo, ...
    ActionInputNames="actPathIn",ObservationInputNames="obsPathIn");

critic2 = rlQValueFunction(criticNet2,obsInfo,actInfo, ...
    ActionInputNames="actPathIn",ObservationInputNames="obsPathIn");

Check the critics with a random observation and action input.

getValue(critic1,{rand(obsInfo.Dimension)},{rand(actInfo.Dimension)})

ans = single
    -0.1330

getValue(critic2,{rand(obsInfo.Dimension)},{rand(actInfo.Dimension)})

ans = single
    -0.1526

To approximate the policy within the actor, use a deep neural network. Since SAC agents use a continuous Gaussian actor, the network must take the observation signal as input and return both a mean value and a standard deviation value for each action. Therefore it must have two output layers (one for the mean values the other for the standard deviation values), each having as many elements as the dimension of the action space.

Do not add a tanhLayer or scalingLayer in the mean output path. The SAC agent internally transforms the unbounded Gaussian distribution to the bounded distribution to compute the probability density function and entropy properly.

Define each network path as an array of layer objects. Get the dimensions of the observation and action spaces from the environment specification objects, and specify a name for the input and output layers, so you can later explicitly associate them with the appropriate channel.

% Define common input path
commonPath = [
    featureInputLayer(prod(obsInfo.Dimension),Name="netObsIn")
    fullyConnectedLayer(400)
    reluLayer(Name="CommonRelu")];

% Define path for mean value
meanPath = [
    fullyConnectedLayer(300,Name="meanIn")
    reluLayer
    fullyConnectedLayer(prod(actInfo.Dimension),Name="MeanOut")
    ];

% Define path for standard deviation
stdPath = [
    fullyConnectedLayer(300,Name="stdIn")
    reluLayer
    fullyConnectedLayer(prod(actInfo.Dimension))
    softplusLayer(Name="StandardDeviationOut")];

% Add layers to layerGraph object 
actorNet = layerGraph(commonPath);
actorNet = addLayers(actorNet,meanPath);
actorNet = addLayers(actorNet,stdPath);

% Connect layers
actorNet = connectLayers(actorNet,"CommonRelu","meanIn/in");
actorNet = connectLayers(actorNet,"CommonRelu","stdIn/in");

% Convert to dlnetwork and display the number of weights.
actorNet = dlnetwork(actorNet);
summary(actorNet)

   Initialized: true

   Number of learnables: 242.4k

   Inputs:
      1   'netObsIn'   2 features

Create the actor using actorNet, the observation and action specification objects, and the names of the input and output layers.

actor = rlContinuousGaussianActor(actorNet, obsInfo, actInfo, ...
    ActionMeanOutputNames="MeanOut",...
    ActionStandardDeviationOutputNames="StandardDeviationOut",...
    ObservationInputNames="netObsIn");

Check your actor with a random input observation.

getAction(actor,{rand(obsInfo.Dimension)})

ans = 1x1 cell array
    {[-0.2589]}

Specify training options for the critics.

criticOptions = rlOptimizerOptions( ...
    Optimizer="adam", ...
    LearnRate=1e-3,... 
    GradientThreshold=1, ...
    L2RegularizationFactor=2e-4);

Specify training options for the actor.

actorOptions = rlOptimizerOptions( ...
    Optimizer="adam", ...
    LearnRate=1e-3,...
    GradientThreshold=1, ...
    L2RegularizationFactor=1e-5);

Specify agent options, including training options for actor and critics.

agentOptions = rlSACAgentOptions;
agentOptions.SampleTime = env.Ts;
agentOptions.DiscountFactor = 0.99;
agentOptions.TargetSmoothFactor = 1e-3;
agentOptions.ExperienceBufferLength = 1e6;
agentOptions.MiniBatchSize = 32;

agentOptions.CriticOptimizerOptions = criticOptions;
agentOptions.ActorOptimizerOptions = actorOptions;

Create SAC agent using actor, critics, and options.

agent = rlSACAgent(actor,[critic1 critic2],agentOptions);

To check your agent, use getAction to return the action from a random observation.

getAction(agent,{rand(obsInfo(1).Dimension)})

ans = 1x1 cell array
    {[0.2504]}

You can now test and train the agent within the environment.

Create SAC Agent using Recurrent Neural Networks

For this example, load the environment used in the example Train DDPG Agent to Control Double Integrator System. The observations from the environment is a vector containing the position and velocity of a mass. The action is a scalar representing a force, applied to the mass, ranging continuously from -2 to 2 Newton.

env = rlPredefinedEnv("DoubleIntegrator-Continuous");
obsInfo = getObservationInfo(env);
actInfo = getActionInfo(env);

A SAC agent uses two Q-value function critics. To approximate each Q-value function, use a neural network. The network for a single-output Q-value function critic must have two input layers, one for the observation and the other for the action, and return a scalar value representing the expected cumulative long-term reward following from the given observation and action.

Define each network path as an array of layer objects, and the dimensions of the observation and action spaces from the environment specification objects. To create a recurrent neural network, use sequenceInputLayer as the input layer and include an lstmLayer as one of the other network layers.

% Define observation path
obsPath = [
    sequenceInputLayer(prod(obsInfo.Dimension),Name="obsIn")
    fullyConnectedLayer(400)
    reluLayer
    fullyConnectedLayer(300,Name = "obsOut")
    ];

% Define action path
actPath = [
    sequenceInputLayer(prod(actInfo.Dimension),Name="actIn")
    fullyConnectedLayer(300,Name="actOut")
    ];

% Define common path
commonPath = [
    concatenationLayer(1,2,Name="cat")
    lstmLayer(16)
    reluLayer
    fullyConnectedLayer(1)
    ];

% Add layers to layergraph object
criticNet = layerGraph(obsPath);
criticNet = addLayers(criticNet,actPath);
criticNet = addLayers(criticNet,commonPath);

% Connect paths
criticNet = connectLayers(criticNet,"obsOut","cat/in1");
criticNet = connectLayers(criticNet,"actOut","cat/in2");

To initialize the network weights differently for the two critics, create two different dlnetwork objects. You must do this because if the agent constructor function does not accept two identical critics.

criticNet1 = dlnetwork(criticNet);
criticNet2 = dlnetwork(criticNet);

Display the number of weights.

summary(criticNet1)

   Initialized: true

   Number of learnables: 161.6k

   Inputs:
      1   'obsIn'   Sequence input with 2 dimensions
      2   'actIn'   Sequence input with 1 dimensions

Create the critic using rlQValueFunction. Use the same network structure for both critics. The SAC agent initializes the two networks using different default parameters.

critic1 = rlQValueFunction(criticNet1,obsInfo,actInfo);
critic2 = rlQValueFunction(criticNet2,obsInfo,actInfo);

Check the critics with a random observation and action input.

getValue(critic1,{rand(obsInfo.Dimension)},{rand(actInfo.Dimension)})

ans = single
    -0.0020

getValue(critic2,{rand(obsInfo.Dimension)},{rand(actInfo.Dimension)})

ans = single
    0.0510

To approximate the policy within the actor, use a deep neural network. Since the critic has a recurrent network, the actor must have a recurrent network too. The network must have two output layers (one for the mean values the other for the standard deviation values), each having as many elements as the dimension of the action space.

Do not add a tanhLayer or scalingLayer in the mean output path. The SAC agent internally transforms the unbounded Gaussian distribution to the bounded distribution to compute the probability density function and entropy properly.

Define each network path as an array of layer objects and specify a name for the input and output layers, so you can later explicitly associate them with the appropriate channel.

% Define common path
commonPath = [
    sequenceInputLayer(prod(obsInfo.Dimension),Name="obsIn")
    fullyConnectedLayer(400)
    lstmLayer(8)
    reluLayer(Name="CommonOut")];

meanPath = [
    fullyConnectedLayer(300,Name="MeanIn")
    reluLayer
    fullyConnectedLayer(prod(actInfo.Dimension),Name="Mean")
    ];

stdPath = [
    fullyConnectedLayer(300,Name="StdIn")
    reluLayer
    fullyConnectedLayer(prod(actInfo.Dimension))
    softplusLayer(Name="StandardDeviation")];

actorNet = layerGraph(commonPath);
actorNet = addLayers(actorNet,meanPath);
actorNet = addLayers(actorNet,stdPath);

actorNet = connectLayers(actorNet,"CommonOut","MeanIn/in");
actorNet = connectLayers(actorNet,"CommonOut","StdIn/in");

% Convert to dlnetwork and display the number of weights.
actorNet = dlnetwork(actorNet);
summary(actorNet)

   Initialized: true

   Number of learnables: 20.2k

   Inputs:
      1   'obsIn'   Sequence input with 2 dimensions

Create the actor using actorNetwork.

actor = rlContinuousGaussianActor(actorNet, obsInfo, actInfo, ...
    ActionMeanOutputNames="Mean",...
    ActionStandardDeviationOutputNames="StandardDeviation",...
    ObservationInputNames="obsIn");

Check your actor with a random input observation.

getAction(actor,{rand(obsInfo.Dimension)})

ans = 1x1 cell array
    {[-0.3476]}

Specify training options for the critics.

criticOptions = rlOptimizerOptions( ...
    Optimizer = "adam", LearnRate = 1e-3,... 
    GradientThreshold = 1, L2RegularizationFactor = 2e-4);

Specify training options for the actor.

actorOptions = rlOptimizerOptions( ...
    Optimizer = "adam", LearnRate = 1e-3,...
    GradientThreshold = 1, L2RegularizationFactor = 1e-5);

Specify agent options. To use a recurrent neural network, you must specify a SequenceLength greater than 1.

agentOptions = rlSACAgentOptions;
agentOptions.SampleTime = env.Ts;
agentOptions.DiscountFactor = 0.99;
agentOptions.TargetSmoothFactor = 1e-3;
agentOptions.ExperienceBufferLength = 1e6;
agentOptions.SequenceLength = 32;
agentOptions.MiniBatchSize = 32;

Create SAC agent using actor, critics, and options.

agent = rlSACAgent(actor,[critic1 critic2],agentOptions);

To check your agent, use getAction to return the action from a random observation.

getAction(agent,{rand(obsInfo.Dimension)})

ans = 1x1 cell array
    {[0.7990]}

You can now test and train the agent within the environment.

Version History

Introduced in R2020b