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rlQAgentOptions

Options for Q-learning agent

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

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 numeric value properties.

PropertyDescription
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.
EpsilonMinMinimum value of Epsilon
EpsilonDecayDecay rate

Epsilon is updated using the following formula when it is greater than EpsilonMin:

Epsilon = Epsilon*(1-EpsilonDecay)

To specify exploration options, use dot notation after creating the rlQAgentOptions object. For example, set the probability threshold to 0.9.

opt = rlQAgentOptions;
opt.EpsilonGreedyExploration.Epsilon = 0.9;

Sample time of agent, specified as a positive scalar.

Within a Simulink environment, the agent gets executed every SampleTime seconds of simulation time.

Within a MATLAB environment, the agent gets executed every time the environment advances. However, SampleTime is the time interval between consecutive elements in the output experience returned by sim or train.

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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This example shows how to create an options object for a Q-Learning agent.

Create an rlQAgentOptions object that specifies the agent sample time.

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

    EpsilonGreedyExploration: [1x1 rl.option.EpsilonGreedyExploration]
                  SampleTime: 0.5000
              DiscountFactor: 0.9900

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

opt.DiscountFactor = 0.95;

See Also

Introduced in R2019a