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Options for DDPG agent


Use an rlDDPGAgentOptions object to specify options for deep deterministic policy gradient (DDPG) agents. To create a DDPG agent, use rlDDPGAgent.

For more information, see Deep Deterministic Policy Gradient Agents.

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



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


opt = rlDDPGAgentOptions(Name,Value) sets option properties using name-value pairs. For example, rlDDPGAgentOptions('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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Noise model options, specified as an OrnsteinUhlenbeckActionNoise object. For more information on the noise model, see Noise Model.

For an agent with multiple actions, if the actions have different ranges and units, it is likely that each action requires different noise model parameters. If the actions have similar ranges and units, you can set the noise parameters for all actions to the same value.

For example, for an agent with two actions, set the standard deviation of each action to a different value while using the same decay rate for both standard deviations.

opt = rlDDPGAgentOptions;
opt.NoiseOptions.StandardDeviation = [0.1 0.2];
opt.NoiseOptions.StandardDeviationDecayRate = 1e-4;

Smoothing factor for target actor and 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 actor and 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.

Option 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.

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

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

Note that if parallel training is enabled (that is if an rlTrainingOptions option object in which the UseParallel property is set to true is passed to train) then NumStepsToLookAhead must be set to 1, otherwise an error is generated. This guarantees that experiences are stored contiguously.


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.

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

rlDDPGAgentDeep deterministic policy gradient reinforcement learning agent


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This example shows how to create a DDPG agent option object.

Create an rlDDPGAgentOptions object that specifies the mini-batch size.

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

                           NoiseOptions: [1x1 rl.option.OrnsteinUhlenbeckActionNoise]
                     TargetSmoothFactor: 1.0000e-03
                  TargetUpdateFrequency: 1
    ResetExperienceBufferBeforeTraining: 1
          SaveExperienceBufferWithAgent: 0
                         SequenceLength: 1
                          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;


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Compatibility Considerations

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Behavior changed in R2021a

Behavior changed in R2020a


[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.

Introduced in R2019a