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Options for SARSA agent

Since R2019a


Use an rlSARSAAgentOptions object to specify options for creating SARSA agents. To create a SARSA agent, use rlSARSAAgent

For more information on SARSA agents, see SARSA Agents.

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



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


opt = rlSARSAAgentOptions(Name=Value) creates the options set opt and sets its properties using one or more name-value arguments. For example, rlSARSAAgentOptions(DiscountFactor=0.95) creates an option set with a discount factor of 0.95. You can specify multiple name-value arguments.


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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 rlSARSAAgentOptions 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.

Example: CriticOptimizerOptions = rlOptimizerOptions(LearnRate=5e-3)

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.

Example: SampleTime=-1

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

Example: DiscountFactor=0.9

Object Functions

rlSARSAAgentSARSA reinforcement learning agent


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Create an rlSARSAAgentOptions object that specifies the agent sample time.

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

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