# rlTD3AgentOptions

## Description

Use an `rlTD3AgentOptions`

object to specify options for
twin-delayed deep deterministic policy gradient (TD3) agents. To create a TD3 agent, use
`rlTD3Agent`

.

For more information see Twin-Delayed Deep Deterministic (TD3) Policy Gradient 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 TD3 agent using all default options. You can
modify the object properties using dot notation.`opt`

= rlTD3AgentOptions

creates the options set `opt`

= rlTD3AgentOptions(`Name=Value`

)`opt`

and sets its properties using one
or more name-value arguments. For example,
`rlTD3AgentOptions(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 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.

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`

`ExplorationModel`

— Exploration noise model options

`GaussianActionNoise`

object (default) | `OrnsteinUhlenbeckActionNoise`

object

Noise model options, specified as a `GaussianActionNoise`

object or
an `OrnsteinUhlenbeckActionNoise`

object. For more information on noise
models, see Noise Models.

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 = rlTD3AgentOptions; opt.ExplorationModel.StandardDeviation = [0.1 0.2]; opt.ExplorationModel.StandardDeviationDecayRate = 1e-4;

To use Ornstein-Uhlenbeck action noise, first create a default
`OrnsteinUhlenbeckActionNoise`

object. Then, specify any nondefault
model properties using dot notation.

opt = rlTD3AgentOptions; opt.ExplorationModel = rl.option.OrnsteinUhlenbeckActionNoise; opt.ExplorationModel.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,
if`NumStepsToLookAhead`

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

`1`

(default) | positive integer

Number of times an agent learns over a data set, 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

`2`

(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=3`

`TargetPolicySmoothModel`

— Target smoothing noise model options

`GaussianActionNoise`

object

Target smoothing noise model options, specified as a
`GaussianActionNoise`

object. This model helps the policy exploit
actions with high Q-value estimates. For more information on noise models, see Noise Models.

For an agent with multiple actions, if the actions have different ranges and units, it is likely that each action requires different smoothing 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 = rlTD3AgentOptions; opt.TargetPolicySmoothModel.StandardDeviation = [0.1 0.2]; opt.TargetPolicySmoothModel.StandardDeviationDecayRate = 1e-4;

`TargetSmoothFactor`

— Smoothing factor for target actor and critic updates

`0.005`

(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

`2`

(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`

and`saveAgentValue`

in an`rlTrainingOptions`

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)
is 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

`rlTD3Agent` | Twin-delayed deep deterministic (TD3) policy gradient reinforcement learning agent |

## Examples

### Create TD3 Agent Options Object

Create an `rlTD3AgentOptions`

object that specifies the mini-batch size.

opt = rlTD3AgentOptions(MiniBatchSize=48)

opt = rlTD3AgentOptions with properties: SampleTime: 1 DiscountFactor: 0.9900 ExplorationModel: [1x1 rl.option.GaussianActionNoise] ExperienceBufferLength: 10000 MiniBatchSize: 48 SequenceLength: 1 ActorOptimizerOptions: [1x1 rl.option.rlOptimizerOptions] CriticOptimizerOptions: [1x2 rl.option.rlOptimizerOptions] NumStepsToLookAhead: 1 NumWarmStartSteps: 48 NumEpoch: 1 MaxMiniBatchPerEpoch: 100 LearningFrequency: -1 PolicyUpdateFrequency: 2 TargetPolicySmoothModel: [1x1 rl.option.GaussianActionNoise] TargetSmoothFactor: 0.0050 TargetUpdateFrequency: 2 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 Models

**Gaussian Action Noise**

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`

.

**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 R2020a**

### R2024a: The `PolicyUpdateFrequency`

property has been redefined

The `PolicyUpdateFrequency`

property has been redefined. Previously,
it was defined as the number of steps between policy updates. Now, it is defined as the
period of the policy (actor) update with respect to the critic update. For example, while a
`PolicyUpdateFrequency`

of `3`

previously meant that
the actor was updated every three steps, it now means that is updated every three critic
updates.

### 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 TD3 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
`rlTD3AgentOptions`

object and use this object to create the TD3
agent.

### R2021a: Properties defining noise probability distribution in the `GaussianActionNoise`

object have changed

The properties defining the probability distribution of the Gaussian action noise model have changed. This noise model is used by TD3 agents for exploration and target policy smoothing.

The

`Variance`

property has been replaced by the`StandardDeviation`

property.The

`VarianceDecayRate`

property has been replaced by the`StandardDeviationDecayRate`

property.The

`VarianceMin`

property has been replaced by the`StandardDeviationMin`

property.

When a `GaussianActionNoise`

noise object saved from a previous
MATLAB release is loaded, the value of `VarianceDecayRate`

is
copied to `StandardDeviationDecayRate`

, while the square root of the
values of `Variance`

and `VarianceMin`

are copied to
`StandardDeviation`

and `StandardDeviationMin`

,
respectively.

The `Variance`

, `VarianceDecayRate`

, and
`VarianceMin`

properties still work, but they are not recommended. To
define the probability distribution of the Gaussian action 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
`rlTD3AgentOptions`

object `td3opt`

.

Not Recommended | Recommended |
---|---|

`td3opt.ExplorationModel.Variance = 0.5;` | ```
td3opt.ExplorationModel.StandardDeviation =
sqrt(0.5);
``` |

`td3opt.ExplorationModel.VarianceDecayRate = 0.1;` | ```
td3opt.ExplorationModel.StandardDeviationDecayRate =
0.1;
``` |

`td3opt.ExplorationModel.VarianceMin = 0.1;` | ```
td3opt.ExplorationModel.StandardDeviationMin =
sqrt(0.1);
``` |

### 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. TD3 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
`rlTD3AgentOptions`

object `td3opt`

.

Not Recommended | Recommended |
---|---|

`td3opt.ExplorationModel.Variance = 0.5;` | ```
td3opt.ExplorationModel.StandardDeviation =
sqrt(0.5);
``` |

`td3opt.ExplorationModel.VarianceDecayRate = 0.1;` | ```
td3opt.ExplorationModel.StandardDeviationDecayRate =
0.1;
``` |

`td3opt.ExplorationModel.VarianceMin = 0.1;` | ```
td3opt.ExplorationModel.StandardDeviationMin =
sqrt(0.1);
``` |

`td3opt.TargetPolicySmoothModel.Variance = 0.5;` | ```
td3opt.TargetPolicySmoothModel.StandardDeviation =
sqrt(0.5);
``` |

```
td3opt.TargetPolicySmoothModel.VarianceDecayRate =
0.1;
``` | ```
td3opt.TargetPolicySmoothModel.StandardDeviationDecayRate =
0.1;
``` |

`td3opt.TargetPolicySmoothModel.VarianceMin = 0.1;` | ```
td3opt.TargetPolicySmoothModel.StandardDeviationMin =
sqrt(0.1);
``` |

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