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rlQAgentOptions

Options for Q-learning agent

Since R2019a

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Description

Use an rlQAgentOptions object to specify options for creating Q-learning agents. To create a Q-learning agent, use rlQAgent

For more information on Q-learning agents, see Q-Learning Agents.

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

Creation

Syntax

opt = rlQAgentOptions
opt = rlQAgentOptions(Name,Value)

Description

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

opt = rlQAgentOptions(Name,Value)sets option properties using name-value pairs. For example, rlQAgentOptions('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

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Options for epsilon-greedy exploration, specified as an EpsilonGreedyExploration object with the following properties.

PropertyDescriptionDefault Value
EpsilonProbability threshold to either randomly select an action or select the action that maximizes the state-action value function. A larger value of Epsilon means that the agent randomly explores the action space at a higher rate.1
EpsilonMinMinimum value of Epsilon0.01
EpsilonDecayDecay rate0.0050

At the end of each training time step, if Epsilon is greater than EpsilonMin, then it is updated using the following formula.

Epsilon = Epsilon*(1-EpsilonDecay)

Note that Epsilon is conserved between the end of an episode and the start of the next one. Therefore, it keeps on uniformly decreasing over multiple episodes until it reaches EpsilonMin.

If your agent converges on local optima too quickly, you can promote agent exploration by increasing Epsilon.

To specify exploration options, use dot notation after creating the rlQAgentOptions object opt. For example, set the epsilon value to 0.9.

opt.EpsilonGreedyExploration.Epsilon = 0.9;

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.

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.

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

Object Functions

rlQAgentQ-learning reinforcement learning agent

Examples

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Open Live Script

Create a rlQAgentOptions object that specifies the agent sample time. 

opt = rlQAgentOptions(SampleTime=0.5)
opt = 
  rlQAgentOptions with properties:

    EpsilonGreedyExploration: [1x1 rl.option.EpsilonGreedyExploration]
      CriticOptimizerOptions: [1x1 rl.option.rlOptimizerOptions]
                  SampleTime: 0.5000
              DiscountFactor: 0.9900
                  InfoToSave: [1x1 struct]

You can modify options using dot notation. For example, set the agent discount factor to 0.95.

opt.DiscountFactor = 0.95;

Version History

Introduced in R2019a

See Also

Objects

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