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rlPGAgentOptions

Options for PG agent

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Description

Use an rlPGAgentOptions object to specify options for policy gradient (PG) agents. To create a PG agent, use rlPGAgent

For more information on PG agents, see REINFORCE Policy Gradient (PG) Agent.

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

Creation

Syntax

opt = rlPGAgentOptions
opt = rlPGAgentOptions(Name=Value)

Description

opt = rlPGAgentOptions creates an rlPGAgentOptions object for use as an argument when creating a PG agent using all default settings. You can modify the object properties using dot notation.

opt = rlPGAgentOptions(Name=Value) creates the options set opt and sets its properties using one or more name-value arguments. For example, rlPGAgentOptions(DiscountFactor=0.95) creates an options set with a discount factor of 0.95. You can specify multiple name-value arguments.

example

Properties

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

Within a MATLAB® environment, the agent is executed every time the environment advances, so, SampleTime does not affect the timing of the agent execution.

Within a Simulink® environment, the RL Agent block that uses the agent 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 or train 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 RL Agent block execution, while for MATLAB environments, this time interval is considered equal to 1.

This property is shared between the agent and the agent options object within the agent. Therefore, if you change it in the agent options object, it gets changed in the agent, and vice versa.

Example: SampleTime=-1

Discount factor applied to future rewards during training, specified as a positive scalar less than or equal to 1.

Example: DiscountFactor=0.9

Entropy loss weight, specified as a scalar value between 0 and 1. A higher entropy loss weight value promotes agent exploration by applying a penalty for being too certain about which action to take. Doing so can help the agent move out of local optima.

When gradients are computed during training, an additional gradient component is computed for minimizing this loss function.

Example: EntropyLossWeight=0.01

Option to use baseline for learning, specified as a logical value. When UseBaseline is true, you must specify a critic network as the baseline function approximator.

In general PG agents work better without a baseline for simpler problems and when using a small actor network.

Example: UseBaseline=false

Actor optimizer options, specified as an rlOptimizerOptions object. It allows you to specify training parameters of the actor approximator such as learning rate, gradient threshold, as well as the optimizer algorithm and its parameters. For more information, see rlOptimizerOptions and rlOptimizer.

Example: ActorOptimizerOptions = rlOptimizerOptions(LearnRate=2e-3)

Critic optimizer options, specified as an rlOptimizerOptions object. It allows you to specify training parameters of the critic approximator such as learning rate, gradient threshold, as well as the optimizer algorithm and its parameters. For more information, see rlOptimizerOptions and rlOptimizer.

Example: CriticOptimizerOptions = rlOptimizerOptions(LearnRate=5e-3)

Options to save additional agent data, specified as a structure containing a field named Optimizer.

You can save an agent object in one of the following ways:

  • Using the save command

  • Specifying saveAgentCriteria and saveAgentValue in an rlTrainingOptions object

  • Specifying an appropriate logging function within a FileLogger object

When you save an agent using any method, the fields in the InfoToSave structure determine whether the corresponding data is saved with the agent. For example, if you set the Optimizer field to true, then the actor and critic optimizers are saved along with the agent.

You can modify the InfoToSave property only after the agent options object is created.

Example: options.InfoToSave.Optimizer=true

Option to save the actor and critic optimizers, specified as a logical value. If you set the Optimizer field to false, then the actor and critic optimizers (which are hidden properties of the agent and can contain internal states) are not saved along with the agent, therefore saving disk space and memory. However, when the optimizers contain internal states, the state of the saved agent is not identical to the state of the original agent.

Example: true

Object Functions

rlPGAgentPolicy gradient (PG) reinforcement learning agent

Examples

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Open Live Script

This example shows how to create and modify a PG agent options object.

Create a PG agent options object, specifying the discount factor.

opt = rlPGAgentOptions(DiscountFactor=0.9)
opt = 
  rlPGAgentOptions with properties:

                SampleTime: 1
            DiscountFactor: 0.9000
         EntropyLossWeight: 0
               UseBaseline: 1
     ActorOptimizerOptions: [1x1 rl.option.rlOptimizerOptions]
    CriticOptimizerOptions: [1x1 rl.option.rlOptimizerOptions]
                InfoToSave: [1x1 struct]

You can modify options using dot notation. For example, set the agent sample time to 0.5.

opt.SampleTime = 0.5;

Version History

Introduced in R2019a

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See Also

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