rlPGAgentOptions

Options for PG agent

Description

Use an rlPGAgentOptions object to specify options when creating a policy gradient (PG) agent. 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 object opt and sets its properties using one or more name-value arguments. For example, rlPGAgentOptions(DiscountFactor=0.95) creates an options object with a discount factor of 0.95. You can specify multiple name-value arguments.

Properties

SampleTime — Sample time of agent

1 (default) | positive scalar | -1

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. If SampleTime is set to -1, in MATLAB environments, the time interval between consecutive elements in the returned output experience is considered equal to 1.

Within a Simulink^® environment, the RL Agent block that uses the agent object executes every SampleTime seconds of simulation time. If SampleTime is set to -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.

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. If SampleTime is a positive scalar, this value is also the time interval between consecutive elements in the output experience returned by sim or train, regardless of the type of environment.

If SampleTime is set to -1, in 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.

This property is shared between the agent and the agent options object within the agent. If you change this property in the agent options object, it also changes in the agent, and vice versa.

Example: SampleTime=-1

DiscountFactor — Discount factor

0.99 (default) | nonnegative scalar less than or equal to 1

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

Example: DiscountFactor=0.9

EntropyLossWeight — Entropy loss weight

0 (default) | scalar value between 0 and 1

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

UseBaseline — Use baseline for learning

true (default) | false

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

ActorOptimizerOptions — Actor optimizer options

rlOptimizerOptions object

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)

CriticOptimizerOptions — Critic optimizer options

rlOptimizerOptions object

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)

InfoToSave — Options to save additional agent data

structure (default)

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

You can save an agent object using one of these methods:

• Use the save command.

• Specify saveAgentCriteria and saveAgentValue in an rlTrainingOptions object.

• Specify 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 saves with the agent. For example, if you set the PolicyState field to true, then the policy state saves along with the agent.

You can modify the InfoToSave property only after you create the agent options object.

Example: options.InfoToSave.Optimizer=true

Optimizer — Option to save actor and critic optimizers

false (default) | 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

rlPGAgent

Policy gradient (PG) reinforcement learning agent

Examples

Create PG Agent Options Object

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: [1×1 rl.option.rlOptimizerOptions]
    CriticOptimizerOptions: [1×1 rl.option.rlOptimizerOptions]
                InfoToSave: [1×1 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

R2022a: Simulation and deployment: UseDeterministicExploitation will be removed

The property UseDeterministicExploitation of the rlPGAgentOptions object will be removed in a future release. Use the UseExplorationPolicy property of rlPGAgent instead.

Previously, you set UseDeterministicExploitation as follows.

• Force the agent to always select the action with maximum likelihood, thereby using a greedy deterministic policy for simulation and deployment.

  agent.AgentOptions.UseDeterministicExploitation = true;

• Allow the agent to select its action by sampling its probability distribution for simulation and policy deployment, thereby using a stochastic policy that explores the observation space.

  agent.AgentOptions.UseDeterministicExploitation = false;

Starting in R2022a, set UseExplorationPolicy as follows.

• Force the agent to always select the action with maximum likelihood, thereby using a greedy deterministic policy for simulation and deployment.

  agent.UseExplorationPolicy = false;

• Allow the agent to select its action by sampling its probability distribution for simulation and policy deployment, thereby using a stochastic policy that explores the observation space.

  agent.UseExplorationPolicy = true;

Similarly to UseDeterministicExploitation, UseExplorationPolicy affects only simulation and deployment; it does not affect training.