rlDDPGAgentOptions

Options for DDPG agent

Since R2019a

Description

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 (DDPG) Agents.

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

Creation

Syntax

opt = rlDDPGAgentOptions

opt = rlDDPGAgentOptions(Name,Value)

Description

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.

Properties

NoiseOptions — Noise model options

OrnsteinUhlenbeckActionNoise object

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;

ActorOptimizerOptions — Actor optimizer options

rlOptimizerOptions object

Actor optimizer options, specified as an rlOptimizerOptions object. It allows you to specify training parameters of the actor approximator such as learning rate, gradient threshold, as well as the optimizer algorithm and its parameters. For more information, see rlOptimizerOptions and rlOptimizer.

CriticOptimizerOptions — Critic optimizer options

rlOptimizerOptions object

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.

BatchDataRegularizerOptions — Batch data regularizer options

[] (default) | rlBehaviorCloningRegularizerOptions object

Batch data regularizer options, specified as an rlBehaviorCloningRegularizerOptions object. These options are typically used to train the agent offline, from existing data. If you leave this option empty, no regularizer is used.

For more information, see rlBehaviorCloningRegularizerOptions.

Example: opt.BatchDataRegularizerOptions = rlBehaviorCloningRegularizerOptions(BehaviorCloningRegularizerWeight=10)

TargetSmoothFactor — Smoothing factor for target actor and critic updates

1e-3 (default) | positive scalar less than or equal to 1

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.

TargetUpdateFrequency — Number of steps between target actor and critic updates

1 (default) | positive integer

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

ResetExperienceBufferBeforeTraining — Option for clearing the experience buffer

false (default) | true

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

SequenceLength — Maximum batch-training trajectory length when using RNN

1 (default) | positive integer

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.

MiniBatchSize — Size of random experience mini-batch

64 (default) | positive integer

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.

NumStepsToLookAhead — Number of future rewards used to estimate the value of the policy

1 (default) | positive integer

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

ExperienceBufferLength — Experience buffer size

10000 (default) | positive integer

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.

SampleTime — Sample time of agent

1 (default) | positive scalar | -1

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.

DiscountFactor — Discount factor

0.99 (default) | positive scalar less than or equal to 1

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

Object Functions

rlDDPGAgent

Deep deterministic policy gradient (DDPG) reinforcement learning agent

Examples

Create DDPG Agent Options Object

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

opt = rlDDPGAgentOptions(MiniBatchSize=48)

opt = 
  rlDDPGAgentOptions with properties:

                           NoiseOptions: [1x1 rl.option.OrnsteinUhlenbeckActionNoise]
                  ActorOptimizerOptions: [1x1 rl.option.rlOptimizerOptions]
                 CriticOptimizerOptions: [1x1 rl.option.rlOptimizerOptions]
            BatchDataRegularizerOptions: []
                     TargetSmoothFactor: 1.0000e-03
                  TargetUpdateFrequency: 1
    ResetExperienceBufferBeforeTraining: 0
                         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;

Algorithms

Noise Model

DDPG agents use an Ornstein-Uhlenbeck action noise model for exploration.

Ornstein-Uhlenbeck Action Noise

An OrnsteinUhlenbeckActionNoise object has the following numeric value properties.

Property	Description	Default Value
InitialAction	Initial value of action	0
Mean	Noise mean value	0
MeanAttractionConstant	Constant specifying how quickly the noise model output is attracted to the mean	0.15
StandardDeviationDecayRate	Decay rate of the standard deviation	0
StandardDeviation	Initial value of noise standard deviation	0.3
StandardDeviationMin	Minimum standard deviation	0

At each sample time step k, the noise value v(k) is updated using the following formula, where Ts is the agent sample time, and the initial value v(1) is defined by the InitialAction parameter.

v(k+1) = v(k) + MeanAttractionConstant.*(Mean - v(k)).*Ts
       + StandardDeviation(k).*randn(size(Mean)).*sqrt(Ts)

At each sample time step, the standard deviation decays as shown in the following code.

decayedStandardDeviation = StandardDeviation(k).*(1 - StandardDeviationDecayRate);
StandardDeviation(k+1) = max(decayedStandardDeviation,StandardDeviationMin);

You can calculate how many samples it will take for the standard deviation to be halved using this simple formula.

halflife = log(0.5)/log(1-StandardDeviationDecayRate);

Note that StandardDeviation 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 StandardDeviationMin.

For continuous action signals, it is important to set the noise standard deviation appropriately to encourage exploration. It is common to set StandardDeviation*sqrt(Ts) to a value between 1% and 10% of your action range.

If your agent converges on local optima too quickly, promote agent exploration by increasing the amount of noise; that is, by increasing the standard deviation. Also, to increase exploration, you can reduce the StandardDeviationDecayRate.

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

R2022a: The default value of the ResetExperienceBufferBeforeTraining property has changed

The default value of the ResetExperienceBufferBeforeTraining has changed from true to false.

When creating a new DDPG agent, if you want to clear the experience buffer before training, you must specify ResetExperienceBufferBeforeTraining as true. For example, before training, set the property using dot notation.

agent.AgentOptions.ResetExperienceBufferBeforeTraining = true;

Alternatively, you can set the property to true in an rlDDPGAgentOptions object and use this object to create the DDPG agent.

R2021a: Property names defining noise probability distribution in the OrnsteinUhlenbeckActionNoise object have changed

The properties defining the probability distribution of the Ornstein-Uhlenbeck (OU) noise model have been renamed. DDPG agents use OU noise for exploration.

• The Variance property has been renamed StandardDeviation.

• The VarianceDecayRate property has been renamed StandardDeviationDecayRate.

• The VarianceMin property has been renamed StandardDeviationMin.

The default values of these properties remain the same. When an OrnsteinUhlenbeckActionNoise noise object saved from a previous MATLAB release is loaded, the values of Variance, VarianceDecayRate, and VarianceMin are copied in the StandardDeviation, StandardDeviationDecayRate, and StandardDeviationMin, respectively.

The Variance, VarianceDecayRate, and VarianceMin properties still work, but they are not recommended. To define the probability distribution of the OU noise model, use the new property names instead.

Update Code

This table shows how to update your code to use the new property names for rlDDPGAgentOptions object ddpgopt.

Not Recommended	Recommended
ddpgopt.NoiseOptions.Variance = 0.5;	ddpgopt.NoiseOptions.StandardDeviation = 0.5;
ddpgopt.NoiseOptions.VarianceDecayRate = 0.1;	ddpgopt.NoiseOptions.StandardDeviationDecayRate = 0.1;
ddpgopt.NoiseOptions.VarianceMin = 0;	ddpgopt.NoiseOptions.StandardDeviationMin = 0;

R2020a: Target update method settings for DDPG agents have changed

Target update method settings for DDPG agents have changed. The following changes require updates to your code:

• The TargetUpdateMethod option has been removed. Now, DDPG agents determine the target update method based on the TargetUpdateFrequency and TargetSmoothFactor option values.

• The default value of TargetUpdateFrequency has changed from 4 to 1.

To use one of the following target update methods, set the TargetUpdateFrequency and TargetSmoothFactor properties as indicated.

Update Method	TargetUpdateFrequency	TargetSmoothFactor
Smoothing	1	Less than 1
Periodic	Greater than 1	1
Periodic smoothing (new method in R2020a)	Greater than 1	Less than 1

The default target update configuration, which is a smoothing update with a TargetSmoothFactor value of 0.001, remains the same.

Update Code

This table shows some typical uses of rlDDPGAgentOptions and how to update your code to use the new option configuration.

Not Recommended	Recommended
opt = rlDDPGAgentOptions('TargetUpdateMethod',"smoothing");	opt = rlDDPGAgentOptions;
opt = rlDDPGAgentOptions('TargetUpdateMethod',"periodic");	opt = rlDDPGAgentOptions; opt.TargetUpdateFrequency = 4; opt.TargetSmoothFactor = 1;
opt = rlDDPGAgentOptions; opt.TargetUpdateMethod = "periodic"; opt.TargetUpdateFrequency = 5;	opt = rlDDPGAgentOptions; opt.TargetUpdateFrequency = 5; opt.TargetSmoothFactor = 1;