rlMaxQPolicy

Policy object to generate discrete max-Q actions for custom training loops and application deployment

Since R2022a

Description

This object implements a max-Q policy, which returns the action that maximizes a discrete action-space Q-value function, given an input observation. You can create an rlMaxQPolicy object from an rlQValueFunction or rlVectorQValueFunction object, or extract it from an rlQAgent, rlDQNAgent or rlSARSAAgent. You can then train the policy object using a custom training loop or deploy it for your application using generatePolicyBlock or generatePolicyFunction. This policy is always deterministic and does not perform any exploration. For more information on policies and value functions, see Create Policies and Value Functions.

Creation

Syntax

policy = rlMaxQPolicy(qValueFunction)

Description

policy = rlMaxQPolicy(qValueFunction) creates the max-Q policy object policy from the discrete action-space Q-value function qValueFunction. It also sets the QValueFunction property of policy to the input argument qValueFunction.

Properties

QValueFunction — Discrete action-space Q-value function

rlQValueFunction object

Discrete action-space Q-value function approximator, specified as an rlQValueFunction or rlVectorQValueFunction object.

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

Action specifications, returned as an rlFiniteSetSpec 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 Max-Q Policy Object from Vector Q-Value Function

Create observation and action specification objects. For this example, define the observation space as a continuous four-dimensional space, so that a single observation is a column vector containing four doubles, and the action space as a finite set consisting of two possible values, -1 and 1.

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

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

Create a vector Q-value function approximator to use as critic. A vector Q-value function must accept an observation as input and return a single vector with as many elements as the number of possible discrete actions.

To model the parametrized vector Q-value function within the critic, use a neural network. Define a single path from the network input to its output as an array of layer objects.

layers = [ 
    featureInputLayer(prod(obsInfo.Dimension))
    fullyConnectedLayer(10)
    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: 72

   Inputs:
      1   'input'   4 features

Create a vector Q-value function using model, and the observation and action specifications.

qValueFcn = rlVectorQValueFunction(model,obsInfo,actInfo)

qValueFcn = 
  rlVectorQValueFunction with properties:

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

Check the critic with a random observation input.

getValue(qValueFcn,{rand(obsInfo.Dimension)})

ans = 2x1 single column vector

    0.6486
   -0.3103

Create a policy object from qValueFcn.

policy = rlMaxQPolicy(qValueFcn)

policy = 
  rlMaxQPolicy with properties:

     QValueFunction: [1x1 rl.function.rlVectorQValueFunction]
      Normalization: "none"
    ObservationInfo: [1x1 rl.util.rlNumericSpec]
         ActionInfo: [1x1 rl.util.rlFiniteSetSpec]
         SampleTime: -1

Check the policy with a random observation input.

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

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

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

Version History

Introduced in R2022a