rlContinuousDeterministicTransitionFunction
Deterministic transition function approximator object for neural network-based environment
Since R2022a
Description
When creating a neural network-based environment using rlNeuralNetworkEnvironment
, you can specify deterministic transition function
approximators using rlContinuousDeterministicTransitionFunction
objects.
A transition function approximator object uses a deep neural network to predict the next observations based on the current observations and actions.
To specify stochastic transition function approximators, use rlContinuousGaussianTransitionFunction
objects.
Creation
Syntax
Description
creates a deterministic transition function approximator object using the deep neural
network tsnFcnAppx
= rlContinuousDeterministicTransitionFunction(net
,observationInfo
,actionInfo
,Name=Value
)net
and sets the ObservationInfo
and
ActionInfo
properties.
When creating a deterministic transition function approximator you must specify the
names of the deep neural network inputs and outputs using the
ObservationInputNames
, ActionInputNames
, and
NextObservationOutputNames
name-value pair arguments.
You can also specify the PredictDiff
and
UseDevice
properties using optional name-value pair arguments. For
example, to use a GPU for prediction, specify UseDevice="gpu"
.
Input Arguments
Properties
Object Functions
rlNeuralNetworkEnvironment | Environment model with deep neural network transition models |
Examples
Version History
Introduced in R2022a