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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

Description

Create Agent from Observation and Action Specifications

example

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.

example

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

example

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

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Agent initialization options, specified as an rlAgentInitializationOptions 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.

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

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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.

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.

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.

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.

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.

  • false — Use the base agent greedy policy when selecting actions.

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

trainTrain reinforcement learning agents within a specified environment
simSimulate trained reinforcement learning agents within specified environment
getActionObtain action from agent or actor given environment observations
getActorGet actor from reinforcement learning agent
setActorSet actor of reinforcement learning agent
getCriticGet critic from reinforcement learning agent
setCriticSet critic of reinforcement learning agent
generatePolicyFunctionCreate function that evaluates trained policy of reinforcement learning agent

Examples

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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. You can ensure reproducibility by fixing the seed of the random generator. To do so, uncomment the following line.

% 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.

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. You can ensure reproducibility by fixing the seed of the random generator. To do so, uncomment the following line.

% 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.

plot(layerGraph(actorNet))

Figure contains an axes object. The axes object contains an object of type graphplot.

plot(layerGraph(criticNet2))

Figure contains an axes object. The axes object contains an object of type graphplot.

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 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);

Create two Q-value critics. First, create a critic deep neural network structure. 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.

% observation input and path
statePath1 = [
    featureInputLayer(prod(obsInfo.Dimension), ...
        'Normalization', 'none','Name','observation')
    fullyConnectedLayer(400,'Name','CriticStateFC1')
    reluLayer('Name','CriticStateRelu1')
    fullyConnectedLayer(300,'Name','CriticStateFC2')
    ];

% action input and path
actionPath1 = [
    featureInputLayer(prod(actInfo.Dimension),'Normalization', ...
        'none','Name','action')
    fullyConnectedLayer(300,'Name','CriticActionFC1')
    ];

% common path
commonPath1 = [
    additionLayer(2,'Name','add')
    reluLayer('Name','CriticCommonRelu1')
    fullyConnectedLayer(1,'Name','CriticOutput')
    ];

% connect layers
criticNet = layerGraph(statePath1);
criticNet = addLayers(criticNet,actionPath1);
criticNet = addLayers(criticNet,commonPath1);
criticNet = connectLayers(criticNet,'CriticStateFC2','add/in1');
criticNet = connectLayers(criticNet,'CriticActionFC1','add/in2');

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(criticNet,obsInfo,actInfo);
critic2 = rlQValueFunction(criticNet,obsInfo,actInfo);

Set some training options for the critics.

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

Create a deep neural network to be used as approximation model within the actor. 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.

statePath = [
    featureInputLayer(prod(obsInfo.Dimension), ...
        'Normalization','none','Name','observation')
    fullyConnectedLayer(400, 'Name','commonFC1')
    reluLayer('Name','CommonRelu')];

meanPath = [
    fullyConnectedLayer(300,'Name','MeanFC1')
    reluLayer('Name','MeanRelu')
    fullyConnectedLayer(prod(actInfo.Dimension),'Name','Mean')
    ];

stdPath = [
    fullyConnectedLayer(300,'Name','StdFC1')
    reluLayer('Name','StdRelu')
    fullyConnectedLayer(prod(actInfo.Dimension),'Name','StdFC2')
    softplusLayer('Name','StandardDeviation')];

actorNetwork = layerGraph(statePath);
actorNetwork = addLayers(actorNetwork,meanPath);
actorNetwork = addLayers(actorNetwork,stdPath);

actorNetwork = connectLayers(actorNetwork,'CommonRelu','MeanFC1/in');
actorNetwork = connectLayers(actorNetwork,'CommonRelu','StdFC1/in');

Create the actor using actorNetwork.

actor = rlContinuousGaussianActor(actorNetwork, obsInfo, actInfo, ...
    'ActionMeanOutputNames','Mean',...
    'ActionStandardDeviationOutputNames','StandardDeviation',...
    'ObservationInputNames','observation');

Set some training options for the actor.

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

Specify agent options.

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
    {[0.1259]}

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

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);

Create two Q-value critics. First, create a critic deep neural network structure. 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. To create a recurrent neural network, use sequenceInputLayer as the input layer and include an lstmLayer as one of the other network layers.

statePath1 = [
    sequenceInputLayer(prod(obsInfo.Dimension), ...
        'Normalization','none','Name','observation')
    fullyConnectedLayer(400,'Name','CriticStateFC1')
    reluLayer('Name','CriticStateRelu1')
    fullyConnectedLayer(300,'Name','CriticStateFC2')
    ];

actionPath1 = [
    sequenceInputLayer(prod(actInfo.Dimension), ...
        'Normalization','none','Name','action')
    fullyConnectedLayer(300,'Name','CriticActionFC1')
    ];

commonPath1 = [
    additionLayer(2,'Name','add')
    lstmLayer(8,'OutputMode','sequence','Name','lstm')
    reluLayer('Name','CriticCommonRelu1')
    fullyConnectedLayer(1,'Name','CriticOutput')
    ];

criticNet = layerGraph(statePath1);
criticNet = addLayers(criticNet,actionPath1);
criticNet = addLayers(criticNet,commonPath1);
criticNet = connectLayers(criticNet,'CriticStateFC2','add/in1');
criticNet = connectLayers(criticNet,'CriticActionFC1','add/in2');

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(criticNet,obsInfo,actInfo);
critic2 = rlQValueFunction(criticNet,obsInfo,actInfo);

Set some training options for the critics.

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

Create an actor 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.

statePath = [
    sequenceInputLayer(prod(obsInfo.Dimension), ...
        'Normalization','none','Name','observation')
    fullyConnectedLayer(400, 'Name','commonFC1')
    lstmLayer(8,'OutputMode','sequence','Name','lstm')    
    reluLayer('Name','CommonRelu')];

meanPath = [
    fullyConnectedLayer(300,'Name','MeanFC1')
    reluLayer('Name','MeanRelu')
    fullyConnectedLayer(prod(actInfo.Dimension),'Name','Mean')
    ];

stdPath = [
    fullyConnectedLayer(300,'Name','StdFC1')
    reluLayer('Name','StdRelu')
    fullyConnectedLayer(prod(actInfo.Dimension),'Name','StdFC2')
    softplusLayer('Name','StandardDeviation')];

actorNetwork = layerGraph(statePath);
actorNetwork = addLayers(actorNetwork,meanPath);
actorNetwork = addLayers(actorNetwork,stdPath);

actorNetwork = connectLayers(actorNetwork,'CommonRelu','MeanFC1/in');
actorNetwork = connectLayers(actorNetwork,'CommonRelu','StdFC1/in');

Create the actor using actorNetwork.

actor = rlContinuousGaussianActor(actorNetwork, obsInfo, actInfo, ...
    'ActionMeanOutputNames','Mean',...
    'ActionStandardDeviationOutputNames','StandardDeviation',...
    'ObservationInputNames','observation');

Set some 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
    {[0.6552]}

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

Version History

Introduced in R2020b