rlStochasticActorPolicy

Policy object to generate stochastic actions for custom training loops and application deployment

Since R2022a

Description

This object implements a stochastic policy, which returns stochastic actions given an input observation, according to a probability distribution. You can create an rlStochasticActorPolicy object from an rlDiscreteCategoricalActor or rlContinuousGaussianActor, or extract it from an rlPGAgent, rlACAgent, rlPPOAgent, rlTRPOAgent, or rlSACAgent. You can then train the policy object using a custom training loop or deploy it for your application using generatePolicyBlock or generatePolicyFunction. If UseMaxLikelihoodAction is set to 1 the policy is deterministic, therefore in this case it does not explore. For more information on policies and value functions, see Create Policies and Value Functions.

Creation

Syntax

policy = rlStochasticActorPolicy(actor)

Description

policy = rlStochasticActorPolicy(actor) creates the stochastic policy object policy from the continuous Gaussian or discrete categorical actor actor. It also sets the Actor property of policy to the input argument actor.

Properties

Actor — Actor

rlDiscreteCategoricalActor object | rlContinuousGaussianActor object

Actor, specified as an rlContinuousGaussianActor or rlDiscreteCategoricalActor object.

UseMaxLikelihoodAction — Option to enable maximum likelihood action

false (default) | true

Option to enable maximum likelihood action, specified as a logical value:

• false — The action is sampled from the probability distribution, which helps exploration.

• true — The action is always the maximum likelihood action. In this case the policy is deterministic and therefore there is no exploration.

Example: true

Normalization — Normalization method

"none" (default) | string array

Normalization method, returned as an array in which each element (one for each input channel defined in the observationInfo and actionInfo properties, in that order) is one of the following values:

• "none" — Do not normalize the input.

• "rescale-zero-one" — Normalize the input by rescaling it to the interval between 0 and 1. The normalized input Y is (U–Min)./(UpperLimit–LowerLimit), where U is the nonnormalized input. Note that nonnormalized input values lower than LowerLimit result in normalized values lower than 0. Similarly, nonnormalized input values higher than UpperLimit result in normalized values higher than 1. Here, UpperLimit and LowerLimit are the corresponding properties defined in the specification object of the input channel.

• "rescale-symmetric" — Normalize the input by rescaling it to the interval between –1 and 1. The normalized input Y is 2(U–LowerLimit)./(UpperLimit–LowerLimit) – 1, where U is the nonnormalized input. Note that nonnormalized input values lower than LowerLimit result in normalized values lower than –1. Similarly, nonnormalized input values higher than UpperLimit result in normalized values higher than 1. Here, UpperLimit and LowerLimit are the corresponding properties defined in the specification object of the input channel.

Note

When you specify the Normalization property of rlAgentInitializationOptions, normalization is applied only to the approximator input channels corresponding to rlNumericSpec specification objects in which both the UpperLimit and LowerLimit properties are defined. After you create the agent, you can use setNormalizer to assign normalizers that use any normalization method. For more information on normalizer objects, see rlNormalizer.

Example: "rescale-symmetric"

ObservationInfo — Observation specifications

rlFiniteSetSpec object | rlNumericSpec object | array

Observation specifications, returned as an rlFiniteSetSpec or rlNumericSpec object or an array containing a mix of such objects. Each element in the array defines the properties of an environment observation channel, such as its dimensions, data type, and name.

ActionInfo — Action specifications

rlFiniteSetSpec object | rlNumericSpec object

Action specifications, returned as an rlFiniteSetSpec or rlNumericSpec object. This object defines the properties of the environment action channel, such as its dimensions, data type, and name.

Note

For this policy object, only one action channel is allowed.

SampleTime — Sample time of policy

1 (default) | positive scalar | -1

Sample time of the policy, specified as a positive scalar or as -1.

Within a MATLAB^® environment, the policy is executed every time you call it within your custom training loop, so, SampleTime does not affect the timing of the policy execution.

Within a Simulink^® environment, the Policy block that uses the policy object executes every SampleTime seconds of simulation time. If SampleTime is -1 the block inherits the sample time from its input signals. Set SampleTime to -1 when the block is a child of an event-driven subsystem.

Note

Set SampleTime to a positive scalar when the block is not a child of an event-driven subsystem. Doing so ensures that the block executes at appropriate intervals when input signal sample times change due to model variations.

Regardless of the type of environment, the time interval between consecutive elements in the output experience returned by sim is always SampleTime.

If SampleTime is -1, for Simulink environments, the time interval between consecutive elements in the returned output experience reflects the timing of the events that trigger the Policy block execution, while for MATLAB environments, this time interval is considered equal to 1.

Example: SampleTime=-1

Object Functions

generatePolicyBlock	Generate Simulink block that evaluates policy of an agent or policy object
generatePolicyFunction	Generate MATLAB function that evaluates policy of an agent or policy object
getAction	Obtain action from agent, actor, or policy object given environment observations
getLearnableParameters	Obtain learnable parameter values from agent, function approximator, or policy object
reset	Reset environment, agent, experience buffer, or policy object
setLearnableParameters	Set learnable parameter values of agent, function approximator, or policy object

Examples

Create Stochastic Actor Policy from Discrete Categorical Actor

Create observation and action specification objects. For this example, define a continuous four-dimensional observation space and a discrete action space having two possible actions.

obsInfo = rlNumericSpec([4 1]);
actInfo = rlFiniteSetSpec([-1 1]);

Alternatively, use getObservationInfo and getActionInfo to extract the specification objects from an environment.

Create a discrete categorical actor. This actor must accept an observation as input and return an output vector in which each element represents the probability of taking the corresponding action.

To approximate the policy function within the actor, use a deep neural network model. Define the network as an array of layer objects, and get the dimension of the observation space and the number of possible actions from the environment specification objects.

layers = [ 
    featureInputLayer(obsInfo.Dimension(1))
    fullyConnectedLayer(16)
    reluLayer
    fullyConnectedLayer(numel(actInfo.Elements)) 
    ];

Convert the network to a dlnetwork object and display the number of weights.

model = dlnetwork(layers);
summary(model)

   Initialized: true

   Number of learnables: 114

   Inputs:
      1   'input'   4 features

Create the actor using model, and the observation and action specifications.

actor = rlDiscreteCategoricalActor(model,obsInfo,actInfo)

actor = 
  rlDiscreteCategoricalActor with properties:

    ObservationInfo: [1x1 rl.util.rlNumericSpec]
         ActionInfo: [1x1 rl.util.rlFiniteSetSpec]
      Normalization: "none"
          UseDevice: "cpu"
         Learnables: {4x1 cell}
              State: {0x1 cell}

To return the probability distribution of the possible actions as a function of a random observation, and given the current network weights, use evaluate.

prb = evaluate(actor,{rand(obsInfo.Dimension)});
prb{1}

ans = 2x1 single column vector

    0.5850
    0.4150

Create a policy object from actor.

policy = rlStochasticActorPolicy(actor)

policy = 
  rlStochasticActorPolicy with properties:

                     Actor: [1x1 rl.function.rlDiscreteCategoricalActor]
    UseMaxLikelihoodAction: 0
             Normalization: "none"
           ObservationInfo: [1x1 rl.util.rlNumericSpec]
                ActionInfo: [1x1 rl.util.rlFiniteSetSpec]
                SampleTime: -1

You can access the policy options using dot notation. For example, set the option to always use the maximum likelihood action, thereby making the policy deterministic.

policy.UseMaxLikelihoodAction = true

policy = 
  rlStochasticActorPolicy with properties:

                     Actor: [1x1 rl.function.rlDiscreteCategoricalActor]
    UseMaxLikelihoodAction: 1
             Normalization: "none"
           ObservationInfo: [1x1 rl.util.rlNumericSpec]
                ActionInfo: [1x1 rl.util.rlFiniteSetSpec]
                SampleTime: -1

Check the policy with a random observation input.

act = getAction(policy,{rand(obsInfo.Dimension)});
act{1}

ans = 
-1

You can now train the policy with a custom training loop and then deploy it to your application.

Version History

Introduced in R2022a