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 (DDPG) Agent.
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
creates the options set opt
= rlDDPGAgentOptions(Name=Value
)opt
and sets its properties using one
or more name-value arguments. For example,
rlDDPGAgentOptions(DiscountFactor=0.95)
creates an option set with a
discount factor of 0.95
. You can specify multiple name-value
arguments.
Properties
SampleTime
— Sample time of agent
1
(default) | positive scalar | -1
Sample time of the agent, specified as a positive scalar or as -1
.
Within a MATLAB® environment, the agent is executed every time the environment advances,
so, SampleTime
does not affect the timing of the agent
execution.
Within a Simulink® environment, the RL Agent block
that uses the agent object executes every SampleTime
seconds of
simulation time. If SampleTime
is -1
the block
inherits the sample time from its input signals. Set SampleTime
to
-1
when the block is a child of an event-driven subsystem.
Note
Set SampleTime
to a positive scalar when the block is not
a child of an event-driven subsystem. Doing so ensures that the block executes
at appropriate intervals when input signal sample times change due to model
variations.
Regardless of the type of environment, the time interval between consecutive elements
in the output experience returned by sim
or
train
is
always SampleTime
.
If SampleTime
is -1
, for Simulink environments, the time interval between consecutive elements in the
returned output experience reflects the timing of the events that trigger the RL Agent block
execution, while for MATLAB environments, this time interval is considered equal to
1
.
This property is shared between the agent and the agent options object within the agent. Therefore, if you change it in the agent options object, it gets changed in the agent, and vice versa.
Example: SampleTime=-1
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.
Example: DiscountFactor=0.9
NoiseOptions
— Noise model options
OrnsteinUhlenbeckActionNoise
object (default) | GaussianActionNoise
object
Noise model options, specified as an OrnsteinUhlenbeckActionNoise
or GaussianActionNoise
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;
To use Gaussian action noise, first create a default
GaussianActionNoise
object. Then, specify any nondefault model
properties using dot
notation.
opt = rlDDPGAgentOptions; opt.NoiseOptions = rl.option.GaussianActionNoise; opt.NoiseOptions.StandardDeviation = 0.05;
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.
Example: ExperienceBufferLength=1e6
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.
Example: MiniBatchSize=128
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.
Example: SequenceLength=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
.
Example: ActorOptimizerOptions =
rlOptimizerOptions(LearnRate=2e-3)
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
.
Example: CriticOptimizerOptions =
rlOptimizerOptions(LearnRate=5e-3)
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. Specifically,
ifNumStepsToLookAhead
is equal
to N, the target value of the policy at a
given step is calculated adding the rewards for the following
N steps and the discounted
estimated value of the state that caused the
N-th reward. This target is also
called N-step return.
Note
When using a recurrent neural network for the critic,
NumStepsToLookAhead
must be
1
.
For more information, see [1], Chapter 7.
Example: NumStepsToLookAhead=3
NumWarmStartSteps
— Minimum number of samples to generate before learning starts
positive integer
Minimum number of samples to generate before learning starts. Use this option to
ensure that learning takes place over a more diverse data set at the beginning of
training. The default, and minimum, value is the value of
MiniBatchSize
. After the software collects a minimum of
NumWarmStartSteps
samples, learning occurs at the intervals
specified by the LearningFrequency
property.
Example: NumWarmStartSteps=20
NumEpoch
— Number of times agent learns over data set stored in the experience buffer
1
(default) | positive integer
Number of times an agent learns over the data set stored in the experience buffer, specified as a positive integer. For off-policy agents that support this property (DQN, DDPG, TD3 and SAC), this value defines the number of passes over the data in the replay buffer at each learning iteration.
Example: NumEpoch=2
MaxMiniBatchPerEpoch
— Maximum number of mini-batches used for learning during a single epoch
100
(default) | positive integer
Maximum number of mini-batches used for learning during a single epoch, specified as a positive integer.
For off-policy agents that support this property (DQN, DDPG, TD3, and SAC), the actual
number of mini-batches used for learning depends on the length of the replay buffer, and
MaxMiniBatchPerEpoch
specifies the upper bound. This value also
specifies the maximum number of gradient steps per learning iteration because the
maximum number of gradient steps is equal to the
MaxMiniBatchPerEpoch
value multiplied by the
NumEpoch
value.
For off-policy agents that support this property, a high
MaxMiniBatchPerEpoch
value means that more time is spent on
learning than collecting new data. Therefore, you can use this parameter to control the
sample efficiency of the learning process.
Example: MaxMiniBatchPerEpoch=200
LearningFrequency
— Minimum number of environment interactions between learning iterations
-1
(default) | positive integer
Minimum number of environment interactions between learning iterations, specified as a
positive integer or -1
. This value defines how many new data samples
need to be generated before learning. For off-policy agents that support this property
(DQN, DDPG, TD3, and SAC), the default value of -1
means that
learning occurs after each episode is finished. Note that learning can start only after
the software collects a minimum of NumWarmStartSteps
samples. It
then occurs at the intervals specified by the LearningFrequency
property.
Example: LearningFrequency=4
PolicyUpdateFrequency
— Period of policy update with respect to critic update
1
(default) | positive integer
Period of policy update with respect to critic update, specified as a positive
integer. This option defines how often the actor is updated with respect to each critic
update. For example, a value of 3
means that the actor is updated
every three critic updates. Updating the actor less frequently than the critic can
improve convergence at the cost of longer training times.
Example: PolicyUpdateFrequency=2
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.
Example: TargetSmoothFactor=1e-2
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.
Example: TargetUpdateFrequency=5
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: BatchDataRegularizerOptions =
rlBehaviorCloningRegularizerOptions(BehaviorCloningRegularizerWeight=10)
ResetExperienceBufferBeforeTraining
— Option for clearing the experience buffer
false
(default) | true
Option for clearing the experience buffer before training, specified as a logical value.
Example: ResetExperienceBufferBeforeTraining=true
InfoToSave
— Options to save additional agent data
structure (default)
Options to save additional agent data, specified as a structure containing the following fields.
Optimizer
PolicyState
Target
ExperienceBuffer
You can save an agent object in one of the following ways:
Using the
save
commandSpecifying
saveAgentCriteria
andsaveAgentValue
in anrlTrainingOptions
objectSpecifying an appropriate logging function within a
FileLogger
object.
When you save an agent using any method, the fields in the
InfoToSave
structure determine whether the
corresponding data is saved with the agent. For example, if you set the
Optimizer
field to true
,
then the actor and critic optimizers are saved along with the agent.
You can modify the InfoToSave
property only after the
agent options object is created.
Example: options.InfoToSave.Optimizer=true
Optimizer
— Option to save actor and critic optimizers
false
(default) | true
Option to save the actor and critic optimizers,
specified as a logical value. If you set the
Optimizer
field to
false
, then the actor and
critic optimizers (which are hidden properties of
the agent and can contain internal states) are not
saved along with the agent, therefore saving disk
space and memory. However, when the optimizers
contains internal states, the state of the saved
agent is not identical to the state of the original
agent.
Example: true
PolicyState
— Option to save state of explorative policy
false
(default) | true
Option to save the state of the explorative policy,
specified as a logical value. If you set the
PolicyState
field to
false
, then the state of the
explorative policy (which is a hidden agent
property) is not saved along with the agent. In this
case, the state of the saved agent is not identical
to the state of the original agent.
Example: true
Target
— Option to save actor and critic targets
false
(default) | true
Option to save the actor and critic targets, specified
as a logical value. If you set the
Target
field to
false
, then the actor and
critic targets (which are hidden agent properties)
are not saved along with the agent. In this case,
when the targets contain internal states, the state
of the saved agent is not identical to the state of
the original agent.
Example: true
ExperienceBuffer
— Option to save experience buffer
false
(default) | true
Option to save the experience buffer, specified as a
logical value. If you set the
PolicyState
field to
false
, then the content of the
experience buffer (which is accessible as an agent
property using dot notation) is not saved along with
the agent. In this case, the state of the saved
agent is not identical to the state of the original
agent.
Example: true
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: SampleTime: 1 DiscountFactor: 0.9900 NoiseOptions: [1x1 rl.option.OrnsteinUhlenbeckActionNoise] ExperienceBufferLength: 10000 MiniBatchSize: 48 SequenceLength: 1 ActorOptimizerOptions: [1x1 rl.option.rlOptimizerOptions] CriticOptimizerOptions: [1x1 rl.option.rlOptimizerOptions] NumStepsToLookAhead: 1 NumWarmStartSteps: 48 NumEpoch: 1 MaxMiniBatchPerEpoch: 100 LearningFrequency: -1 PolicyUpdateFrequency: 1 TargetSmoothFactor: 1.0000e-03 TargetUpdateFrequency: 1 BatchDataRegularizerOptions: [] ResetExperienceBufferBeforeTraining: 0 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
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
.
A GaussianActionNoise
object has the following numeric value
properties.
Property | Description | Default Value
(ExplorationModel ) | Default Value
(TargetPolicySmoothModel ) |
---|---|---|---|
Mean | Noise mean value | 0 | 0 |
StandardDeviationDecayRate | Decay rate of the standard deviation | 0 | 0 |
StandardDeviation | Initial value of noise standard deviation | sqrt(0.1) | sqrt(0.2) |
StandardDeviationMin | Minimum standard deviation, which must be less than
StandardDeviation | 0.01 | 0.01 |
LowerLimit | Noise sample lower limit | -Inf | -0.5 |
UpperLimit | Noise sample upper limit | Inf | 0.5 |
At each time step k
, the Gaussian noise v
is
sampled as shown in the following code.
w = Mean + randn(ActionSize).*StandardDeviation(k); v(k+1) = min(max(w,LowerLimit),UpperLimit);
Where the initial value v(1) is defined by the InitialAction
parameter. 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);
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
.
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; |
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