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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)

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

opt = rlDQNAgentOptions;
opt.EpsilonGreedyExploration.Epsilon = 0.9;

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

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.

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.

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

Flag for saving the experience buffer data when saving the agent, specified as a logical value. This option applies both when saving candidate agents during training and when saving agents using the save function.

For some agents, such as those with a large experience buffer and image-based observations, the memory required for saving their experience buffer is large. In such cases, to not save the experience buffer data, set SaveExperienceBufferWithAgent to false.

If you plan to further train your saved agent, you can start training with the previous experience buffer as a starting point. In this case, set SaveExperienceBufferWithAgent to true.

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 steps to look ahead during training, specified as a positive integer.

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 updates the critic using a mini-batch of experiences randomly sampled from the buffer.

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

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]
                         SequenceLength: 1
                     TargetSmoothFactor: 1.0000e-03
                  TargetUpdateFrequency: 1
    ResetExperienceBufferBeforeTraining: 1
          SaveExperienceBufferWithAgent: 0
                          MiniBatchSize: 48
                    NumStepsToLookAhead: 1
                 ExperienceBufferLength: 10000
                             SampleTime: 1
                         DiscountFactor: 0.9900

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

opt.SampleTime = 0.5;

Compatibility Considerations

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Behavior changed in R2020a

See Also

Introduced in R2019a