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rlDQNAgentOptions

Options for DQN agent

Description

Use an rlDQNAgentOptions object to specify options for deep Q-network (DQN) agents. To create a DQN agent, use rlDQNAgent.

For more information, see Deep Q-Network Agents.

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

Creation

Description

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

example

opt = rlDQNAgentOptions(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

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Flag for using double DQN for value function target updates, specified as a logical value. For most application set UseDoubleDQN to "on". For more information, see Deep Q-Network Agents.

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 rlDQNAgentOptions 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.

Smoothing factor for target critic updates, specified as a positive scalar less than or equal to 1. For more information, see Target Update Methods.

Number of steps between target critic updates, specified as a positive integer. For more information, see Target Update Methods.

Option for clearing the experience buffer before training, specified as a logical value.

Maximum batch-training trajectory length when using a recurrent neural network for the critic, specified as a positive integer. This value must be greater than 1 when using a recurrent neural network for the critic and 1 otherwise.

Size of random experience mini-batch, specified as a positive integer. During each training episode, the agent randomly samples experiences from the experience buffer when computing gradients for updating the critic properties. Large mini-batches reduce the variance when computing gradients but increase the computational effort.

When using a recurrent neural network for the critic, MiniBatchSize is the number of experience trajectories in a batch, where each trajectory has length equal to SequenceLength.

Number of future rewards used to estimate the value of the policy, specified as a positive integer. For more information, see chapter 7 of [1].

N-step Q learning is not supported when using a recurrent neural network for the critic. In this case, NumStepsToLookAhead must be 1.

Experience buffer size, specified as a positive integer. During training, the agent computes updates using a mini-batch of experiences randomly sampled from the buffer.

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

rlDQNAgentDeep Q-network reinforcement learning agent

Examples

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This example shows how to create a DQN agent options object.

Create an rlDQNAgentOptions object that specifies the agent mini-batch size.

opt = rlDQNAgentOptions('MiniBatchSize',48)
opt = 
  rlDQNAgentOptions with properties:

                           UseDoubleDQN: 1
               EpsilonGreedyExploration: [1x1 rl.option.EpsilonGreedyExploration]
                 CriticOptimizerOptions: [1x1 rl.option.rlOptimizerOptions]
                     TargetSmoothFactor: 1.0000e-03
                  TargetUpdateFrequency: 1
    ResetExperienceBufferBeforeTraining: 1
                         SequenceLength: 1
                          MiniBatchSize: 48
                    NumStepsToLookAhead: 1
                 ExperienceBufferLength: 10000
                             SampleTime: 1
                         DiscountFactor: 0.9900
                             InfoToSave: [1x1 struct]

You can modify options using dot notation. For example, set the agent sample time to 0.5.

opt.SampleTime = 0.5;

References

[1] Sutton, Richard S., and Andrew G. Barto. Reinforcement Learning: An Introduction. Second edition. Adaptive Computation and Machine Learning. Cambridge, Mass: The MIT Press, 2018.

Version History

Introduced in R2019a

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Behavior changed in R2020a

See Also