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Options for Q-learning agent


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.



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.


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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)

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;

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


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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