rlDDPGAgentOptions
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
Description
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
sets option properties using
name-value pairs. For example,
opt
= rlDDPGAgentOptions(Name,Value
)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.
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 R2019aR2022a: 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 renamedStandardDeviation
.The
VarianceDecayRate
property has been renamedStandardDeviationDecayRate
.The
VarianceMin
property has been renamedStandardDeviationMin
.
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.
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 theTargetUpdateFrequency
andTargetSmoothFactor
option values.The default value of
TargetUpdateFrequency
has changed from4
to1
.
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.
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; |
Abrir ejemplo
Tiene una versión modificada de este ejemplo. ¿Desea abrir este ejemplo con sus modificaciones?
Comando de MATLAB
Ha hecho clic en un enlace que corresponde a este comando de MATLAB:
Ejecute el comando introduciéndolo en la ventana de comandos de MATLAB. Los navegadores web no admiten comandos de MATLAB.
Select a Web Site
Choose a web site to get translated content where available and see local events and offers. Based on your location, we recommend that you select: .
You can also select a web site from the following list:
How to Get Best Site Performance
Select the China site (in Chinese or English) for best site performance. Other MathWorks country sites are not optimized for visits from your location.
Americas
- América Latina (Español)
- Canada (English)
- United States (English)
Europe
- Belgium (English)
- Denmark (English)
- Deutschland (Deutsch)
- España (Español)
- Finland (English)
- France (Français)
- Ireland (English)
- Italia (Italiano)
- Luxembourg (English)
- Netherlands (English)
- Norway (English)
- Österreich (Deutsch)
- Portugal (English)
- Sweden (English)
- Switzerland
- United Kingdom (English)