rlSACAgentOptions
Description
Use an rlSACAgentOptions
object to specify options for soft
actor-critic (SAC) agents. To create a SAC agent, use rlSACAgent
.
For more information, see Soft Actor-Critic (SAC) 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 SAC agent using all default options. You can
modify the object properties using dot notation.opt
= rlSACAgentOptions
sets option properties using
name-value pairs. For example, opt
= rlSACAgentOptions(Name,Value
)rlSACAgentOptions('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
EntropyWeightOptions
— Entropy tuning options
EntropyWeightOptions
object
Entropy tuning options, specified as an EntropyWeightOptions
object with the following properties.
EntropyWeight
— Initial entropy component weight
1
(default) | positive scalar
Initial entropy component weight, specified as a positive scalar.
LearnRate
— Optimizer learning rate
3e-4
(default) | nonnegative scalar
Optimizer learning rate, specified as a nonnegative scalar. If
LearnRate
is zero, the EntropyWeight
value is fixed during training and the TargetEntropy
value is
ignored.
TargetEntropy
— Target entropy value
[]
(default) | scalar
Target entropy value for tuning entropy weight, specified as a scalar. A higher target entropy value encourages more exploration.
If you do not specify TargetEntropy
, the agent uses
–A as the target value, where A is the
number of actions.
Algorithm
— Algorithm to tune entropy
"adam"
(default) | "sgdm"
| "rmsprop"
Algorithm to tune entropy, specified as one of the following strings.
"adam"
— Use the Adam optimizer. You can specify the decay rates of the gradient and squared gradient moving averages using theGradientDecayFactor
andSquaredGradientDecayFactor
fields of theOptimizerParameters
option."sgdm"
— Use the stochastic gradient descent with momentum (SGDM) optimizer. You can specify the momentum value using theMomentum
field of theOptimizerParameters
option."rmsprop"
— Use the RMSProp optimizer. You can specify the decay rate of the squared gradient moving average using theSquaredGradientDecayFactor
fields of theOptimizerParameters
option.
For more information about these optimizers, see Stochastic Gradient Descent in Deep Learning Toolbox™.
GradientThreshold
— Threshold value for gradient
Inf
(default) | positive scalar
Threshold value for the entropy gradient, specified as Inf
or a positive scalar. If the gradient exceeds this value, the gradient is
clipped.
OptimizerParameters
— Applicable parameters for optimizer
OptimizerParameters
object
Applicable parameters for the optimizer, specified as an
OptimizerParameters
object with the following parameters.
The default parameter values work well for most problems.
Parameter | Description | Default |
---|---|---|
Momentum | Contribution of previous step, specified as a scalar from 0 to 1. A value of 0 means no contribution from the previous step. A value of 1 means maximal contribution. This parameter applies only
when | 0.9 |
Epsilon | Denominator offset, specified as a positive scalar. The optimizer adds this offset to the denominator in the network parameter updates to avoid division by zero. This parameter applies
only when | 1e-8 |
GradientDecayFactor | Decay rate of gradient moving average, specified as a positive scalar from 0 to 1. This parameter applies only when
| 0.9 |
SquaredGradientDecayFactor | Decay rate of squared gradient moving average, specified as a positive scalar from 0 to 1. This parameter applies only
when | 0.999 |
When a particular property of OptimizerParameters
is not
applicable to the optimizer type specified in the Algorithm
option, that property is set to "Not applicable"
.
To change the default values, access the properties of
OptimizerParameters
using dot notation.
opt = rlSACAgentOptions; opt.EntropyWeightOptions.OptimizerParameters.GradientDecayFactor = 0.95;
UseCriticTargetEntropy
— Option to use entropy in the critic targets
true
(default) | false
Option to use entropy in the critic targets, specified as a either
true
(default, entropy is used) or false
(entropy is not used). Note that this option does not affect entropy usage in the
actor.
Example: UseCriticTargetEntropy=false
PolicyUpdateFrequency
— Number of steps between actor policy updates
1
(default) | positive integer
Number of steps between actor policy updates, specified as a positive integer. For more information, see Training Algorithm.
CriticUpdateFrequency
— Number of steps between critic updates
1
(default) | positive integer
Number of steps between critic updates, specified as a positive integer. For more information, see Training Algorithm.
NumWarmStartSteps
— Number of actions to take before updating actor and critic
positive integer
Number of actions to take before updating actor and critics, specified as a positive
integer. By default, the NumWarmStartSteps
value is equal to the
MiniBatchSize
value.
NumGradientStepsPerUpdate
— Number of gradient steps when updating actor and critics
1
(default) | positive integer
Number of gradient steps to take when updating actor and critics, specified as a positive integer.
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) | rlConservativeQLearningOptions
object | rlBehaviorCloningRegularizerOptions
object
Batch data regularizer options, specified as an
rlConservativeQLearningOptions
or 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 rlConservativeQLearningOptions
and rlBehaviorCloningRegularizerOptions
.
Example: opt.BatchDataRegularizerOptions =
rlConservativeQLearningOptions(NumSampledActions=20)
TargetSmoothFactor
— Smoothing factor for target critic updates
1e-3
(default) | positive scalar less than or equal to 1
Smoothing factor for target 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 critic updates
1
(default) | positive integer
Number of steps between target 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 actor and critics. 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
rlSACAgent | Soft actor-critic (SAC) reinforcement learning agent |
Examples
Create SAC Agent Options Object
Create a SAC agent options object, specifying the discount factor.
opt = rlSACAgentOptions(DiscountFactor=0.95)
opt = rlSACAgentOptions with properties: EntropyWeightOptions: [1x1 rl.option.EntropyWeightOptions] UseCriticTargetEntropy: 1 PolicyUpdateFrequency: 1 CriticUpdateFrequency: 1 NumWarmStartSteps: 64 NumGradientStepsPerUpdate: 1 ActorOptimizerOptions: [1x1 rl.option.rlOptimizerOptions] CriticOptimizerOptions: [1x2 rl.option.rlOptimizerOptions] BatchDataRegularizerOptions: [] TargetSmoothFactor: 1.0000e-03 TargetUpdateFrequency: 1 ResetExperienceBufferBeforeTraining: 0 SequenceLength: 1 MiniBatchSize: 64 NumStepsToLookAhead: 1 ExperienceBufferLength: 10000 SampleTime: 1 DiscountFactor: 0.9500 InfoToSave: [1x1 struct]
You can modify options using dot notation. For example, set the agent sample time to 0.5
.
opt.SampleTime = 0.5;
For SAC agents, configure the entropy weight optimizer using the options in EntropyWeightOptions
. For example, set the target entropy value to –5
.
opt.EntropyWeightOptions.TargetEntropy = -5;
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 R2020bR2022a: Simulation and deployment: UseDeterministicExploitation
will be removed
The property UseDeterministicExploitation
of the
rlSACAgentOptions
object will be removed in a future release. Use the
UseExplorationPolicy
property of rlSACAgent
instead.
Previously, you set UseDeterministicExploitation
as follows.
Force the agent to always select the action with maximum likelihood, thereby using a greedy deterministic policy for simulation and deployment.
agent.AgentOptions.UseDeterministicExploitation = true;
Allow the agent to select its action by sampling its probability distribution for simulation and policy deployment, thereby using a stochastic policy that explores the observation space.
agent.AgentOptions.UseDeterministicExploitation = false;
Starting in R2022a, set UseExplorationPolicy
as follows.
Force the agent to always select the action with maximum likelihood, thereby using a greedy deterministic policy for simulation and deployment.
agent.UseExplorationPolicy = false;
Allow the agent to select its action by sampling its probability distribution for simulation and policy deployment, thereby using a stochastic policy that explores the observation space.
agent.UseExplorationPolicy = true;
Similarly to UseDeterministicExploitation
,
UseExplorationPolicy
affects only simulation and deployment; it does
not affect training.
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 SAC 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
rlSACAgentOptions
object and use this object to create the SAC
agent.
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