rlDDPGAgentOptions

Options for DDPG agent

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 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.

example

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

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`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 variance of each action to a different value while using the same decay rate for both variances.

opt = rlDDPGAgentOptions;
opt.ExplorationModel.Variance = [0.1 0.2];
opt.ExplorationModel.VarianceDecayRate = 1e-4;

`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` — Flag for clearing the experience buffer
`true` (default) | `false`

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

`SaveExperienceBufferWithAgent` — Flag for saving the experience buffer
`false` (default) | `true`

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.

`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 steps ahead
`1` (default) | positive integer

Number of future rewards used to estimate the value of the policy, specified as a positive integer. See [1], (Chapter 7), for more detail.

`ExperienceBufferLength` — Experience buffer size
`10000` (default) | positive integer

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

`SampleTime` — Sample time of agent
`1` (default) | positive scalar

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.

`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 reinforcement learning agent

Examples

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Create DDPG Agent Options Object

Open Live Script

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
                          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;

Algorithms

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Noise Model

An OrnsteinUhlenbeckActionNoise object has the following numeric value properties.

Property	Description	Default Value
`InitialAction`	Initial value of action for noise model	`0`
`Mean`	Noise model mean	`0`
`MeanAttractionConstant`	Constant specifying how quickly the noise model output is attracted to the mean	`0.15`
`VarianceDecayRate`	Decay rate of the variance	`0`
`Variance`	Noise model variance	`0.3`
`VarianceMin`	Minimum variance	`0`

At each sample time step, the noise model is updated using the following formula, where Ts is the agent sample time.

x(k) = x(k-1) + MeanAttractionConstant.*(Mean - x(k-1)).*Ts
       + Variance.*randn(size(Mean)).*sqrt(Ts)

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

decayedVariance = Variance.*(1 - VarianceDecayRate);
Variance = max(decayedVariance,VarianceMin);

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

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

For continuous action signals, it is important to set the noise variance appropriately to encourage exploration. It is common to have Variance*sqrt(Ts) be 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 variance. Also, to increase exploration, you can reduce the VarianceDecayRate.

Compatibility Considerations

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Target update method settings for DDPG agents have changed

Behavior changed in R2020a

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;

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;

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.

Documentation

rlDDPGAgentOptions

Description

Creation

Syntax

Description

Properties

`NoiseOptions` — Noise model options
`OrnsteinUhlenbeckActionNoise` object

`TargetSmoothFactor` — Smoothing factor for target actor and critic updates
`1e-3` (default) | positive scalar less than or equal to 1

`TargetUpdateFrequency` — Number of steps between target actor and critic updates
`1` (default) | positive integer

`ResetExperienceBufferBeforeTraining` — Flag for clearing the experience buffer
`true` (default) | `false`

`SaveExperienceBufferWithAgent` — Flag for saving the experience buffer
`false` (default) | `true`

`MiniBatchSize` — Size of random experience mini-batch
`64` (default) | positive integer

`NumStepsToLookAhead` — Number of steps ahead
`1` (default) | positive integer

`ExperienceBufferLength` — Experience buffer size
`10000` (default) | positive integer

`SampleTime` — Sample time of agent
`1` (default) | positive scalar

`DiscountFactor` — Discount factor
`0.99` (default) | positive scalar less than or equal to 1

Object Functions

Examples

Create DDPG Agent Options Object

Algorithms

Noise Model

Compatibility Considerations

Target update method settings for DDPG agents have changed

References

See Also

Topics

Reinforcement Learning Toolbox Documentation

Support

Documentation

rlDDPGAgentOptions

Description

Creation

Syntax

Description

Properties

NoiseOptions — Noise model options OrnsteinUhlenbeckActionNoise object

TargetSmoothFactor — Smoothing factor for target actor and critic updates 1e-3 (default) | positive scalar less than or equal to 1

TargetUpdateFrequency — Number of steps between target actor and critic updates 1 (default) | positive integer

ResetExperienceBufferBeforeTraining — Flag for clearing the experience buffer true (default) | false

SaveExperienceBufferWithAgent — Flag for saving the experience buffer false (default) | true

MiniBatchSize — Size of random experience mini-batch 64 (default) | positive integer

NumStepsToLookAhead — Number of steps ahead 1 (default) | positive integer

ExperienceBufferLength — Experience buffer size 10000 (default) | positive integer

SampleTime — Sample time of agent 1 (default) | positive scalar

DiscountFactor — Discount factor 0.99 (default) | positive scalar less than or equal to 1

Object Functions

Examples

Create DDPG Agent Options Object

Algorithms

Noise Model

Compatibility Considerations

Target update method settings for DDPG agents have changed

References

See Also

Topics

Reinforcement Learning Toolbox Documentation

Support

`NoiseOptions` — Noise model options
`OrnsteinUhlenbeckActionNoise` object

`TargetSmoothFactor` — Smoothing factor for target actor and critic updates
`1e-3` (default) | positive scalar less than or equal to 1

`TargetUpdateFrequency` — Number of steps between target actor and critic updates
`1` (default) | positive integer

`ResetExperienceBufferBeforeTraining` — Flag for clearing the experience buffer
`true` (default) | `false`

`SaveExperienceBufferWithAgent` — Flag for saving the experience buffer
`false` (default) | `true`

`MiniBatchSize` — Size of random experience mini-batch
`64` (default) | positive integer

`NumStepsToLookAhead` — Number of steps ahead
`1` (default) | positive integer

`ExperienceBufferLength` — Experience buffer size
`10000` (default) | positive integer

`SampleTime` — Sample time of agent
`1` (default) | positive scalar

`DiscountFactor` — Discount factor
`0.99` (default) | positive scalar less than or equal to 1