rlACAgentOptions

Options for AC agent

Description

Use an rlACAgentOptions object to specify options for creating actor-critic (AC) agents. To create an actor-critic agent, use rlACAgent

For more information see Actor-Critic Agents.

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

Creation

Syntax

opt = rlACAgentOptions

opt = rlACAgentOptions(Name,Value)

Description

opt = rlACAgentOptions creates a default option set for an AC agent. You can modify the object properties using dot notation.

opt = rlACAgentOptions(Name,Value) sets option properties using name-value pairs. For example, rlDQNAgentOptions('DiscountFactor',0.95) creates an option set with a discount factor of 0.95. You can specify multiple name-value pairs. Enclose each property name in quotes.

Properties

NumStepsToLookAhead — Number of steps ahead

32 (default) | positive integer

Number of steps the agent interacts with the environment before learning from its experience, specified as a positive integer. When the agent uses a recurrent neural network, NumStepsToLookAhead is treated as the training trajectory length.

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.

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.

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.

SampleTime — Sample time of agent

1 (default) | positive scalar | -1

Sample time of agent, specified as a positive scalar or as -1. Setting this parameter to -1 allows for event-based simulations.

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.

DiscountFactor — Discount factor

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

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

Object Functions

rlACAgent

Actor-critic reinforcement learning agent

Examples

Create AC Agent Options Object

Create an AC agent options object, specifying the discount factor.

opt = rlACAgentOptions('DiscountFactor',0.95)

opt = 
  rlACAgentOptions with properties:

       NumStepsToLookAhead: 32
         EntropyLossWeight: 0
     ActorOptimizerOptions: [1x1 rl.option.rlOptimizerOptions]
    CriticOptimizerOptions: [1x1 rl.option.rlOptimizerOptions]
                SampleTime: 1
            DiscountFactor: 0.9500
                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

R2022a: Simulation and deployment: UseDeterministicExploitation will be removed

The property UseDeterministicExploitation of the rlACAgentOptions object will be removed in a future release. Use the UseExplorationPolicy property of rlACAgent 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.

R2020b: Default value for NumStepsToLookAhead changed to 32

A value of 32 for this property should work better than 1 for most environments. If you nave MATLAB R2020b or a later version and you want to reproduce how rlACAgent behaved on versions prior to R2020b, set this value to 1.