# accelerate

(Not recommended) Option to accelerate computation of gradient for approximator object based on neural network

*Since R2022a*

`accelerate`

is not recommended. Use `dlaccelerate`

on
your loss function instead. For more information, see accelerate is not recommended.

## Description

returns the new neural-network-based function approximator object
`newAppx`

= accelerate(`oldAppx`

,`useAcceleration`

)`newAppx`

, which has the same configuration as the original object,
`oldAppx`

, and the option to accelerate the gradient computation set to
the logical value `useAcceleration`

.

## Examples

### Accelerate Gradient Computation for a Q-Value Function

Create observation and action specification objects (or
alternatively use `getObservationInfo`

and
`getActionInfo`

to extract the specification objects from an
environment). For this example, define an observation space with two channels. The first
channel carries an observation from a continuous four-dimensional space. The second
carries a discrete scalar observation that can be either zero or one. Finally, the
action space is a three-dimensional vector in a continuous action space.

obsInfo = [rlNumericSpec([4 1]) rlFiniteSetSpec([0 1])]; actInfo = rlNumericSpec([3 1]);

To approximate the Q-value function within the critic, create a recurrent deep neural network. The output layer must be a scalar expressing the value of executing the action given the observation.

Define each network path as an array of layer objects. Get the dimensions of the
observation and action spaces from the environment specification objects, and specify a
name for the input layers, so you can later explicitly associate them with the
appropriate environment channel. Since the network is recurrent, use
`sequenceInputLayer`

as the input layer and include an
`lstmLayer`

as one of the other network layers.

% Define paths inPath1 = [ sequenceInputLayer( ... prod(obsInfo(1).Dimension), ... Name="netObsIn1") fullyConnectedLayer(5,Name="infc1") ]; inPath2 = [ sequenceInputLayer( ... prod(obsInfo(2).Dimension), ... Name="netObsIn2") fullyConnectedLayer(5,Name="infc2") ]; inPath3 = [ sequenceInputLayer( ... prod(actInfo(1).Dimension), ... Name="netActIn") fullyConnectedLayer(5,Name="infc3") ]; % Concatenate 3 previous layer outputs along dim 1 jointPath = [ concatenationLayer(1,3,Name="cct") tanhLayer lstmLayer(8,"OutputMode","sequence") fullyConnectedLayer(1,Name="jntfc") ];

Assemble `dlnetwork`

object.

net = dlnetwork; net = addLayers(net,inPath1); net = addLayers(net,inPath2); net = addLayers(net,inPath3); net = addLayers(net,jointPath);

Connect layers.

net = connectLayers(net,"infc1","cct/in1"); net = connectLayers(net,"infc2","cct/in2"); net = connectLayers(net,"infc3","cct/in3");

Plot network.

plot(net)

Initialize network and display the number of weights.

net = initialize(net); summary(net)

Initialized: true Number of learnables: 832 Inputs: 1 'netObsIn1' Sequence input with 4 dimensions 2 'netObsIn2' Sequence input with 1 dimensions 3 'netActIn' Sequence input with 3 dimensions

Create the critic with `rlQValueFunction`

, using the network, and
the observation and action specification objects.

critic = rlQValueFunction(net, ... obsInfo, ... actInfo, ... ObservationInputNames=["netObsIn1","netObsIn2"], ... ActionInputNames="netActIn");

To return the value of the actions as a function of the current observation, use
`getValue`

or `evaluate`

.

val = evaluate(critic, ... { rand(obsInfo(1).Dimension), ... rand(obsInfo(2).Dimension), ... rand(actInfo(1).Dimension) })

`val = `*1×1 cell array*
{[0.0089]}

When you use `evaluate`

, the result is a single-element cell array
containing the value of the action in the input, given the observation.

val{1}

`ans = `*single*
0.0089

Calculate the gradients of the sum of the three outputs with respect to the inputs, given a random observation.

gro = gradient(critic,"output-input", ... { rand(obsInfo(1).Dimension) , ... rand(obsInfo(2).Dimension) , ... rand(actInfo(1).Dimension) } )

`gro=`*3×1 cell array*
{4×1 single}
{[ -0.0945]}
{3×1 single}

The result is a cell array with as many elements as the number of input channels. Each element contains the derivatives of the sum of the outputs with respect to each component of the input channel. Display the gradient with respect to the element of the second channel.

gro{2}

`ans = `*single*
-0.0945

Obtain the gradient with respect of five independent sequences, each one made of nine sequential observations.

gro_batch = gradient(critic,"output-input", ... { rand([obsInfo(1).Dimension 5 9]) , ... rand([obsInfo(2).Dimension 5 9]) , ... rand([actInfo(1).Dimension 5 9]) } )

`gro_batch=`*3×1 cell array*
{4×1×5×9 single}
{1×1×5×9 single}
{3×1×5×9 single}

Display the derivative of the sum of the outputs with respect to the third observation element of the first input channel, after the seventh sequential observation in the fourth independent batch.

gro_batch{1}(3,1,4,7)

`ans = `*single*
0.0693

Set the option to accelerate the gradient computations.

critic = accelerate(critic,true);

Calculate the gradients of the sum of the outputs with respect to the parameters, given a random observation.

grp = gradient(critic,"output-parameters", ... { rand(obsInfo(1).Dimension) , ... rand(obsInfo(2).Dimension) , ... rand(actInfo(1).Dimension) } )

`grp=`*11×1 cell array*
{ 5×4 single }
{ 5×1 single }
{ 5×1 single }
{ 5×1 single }
{ 5×3 single }
{ 5×1 single }
{32×15 single }
{32×8 single }
{32×1 single }
{[-0.0140 -0.0424 -0.0676 -0.0266 -0.0166 -0.0915 0.0405 0.0315]}
{[ 1]}

Each array within a cell contains the gradient of the sum of the outputs with respect to a group of parameters.

grp_batch = gradient(critic,"output-parameters", ... { rand([obsInfo(1).Dimension 5 9]) , ... rand([obsInfo(2).Dimension 5 9]) , ... rand([actInfo(1).Dimension 5 9]) } )

`grp_batch=`*11×1 cell array*
{ 5×4 single }
{ 5×1 single }
{ 5×1 single }
{ 5×1 single }
{ 5×3 single }
{ 5×1 single }
{32×15 single }
{32×8 single }
{32×1 single }
{[-2.0333 -10.3220 -10.6084 -1.2850 -4.4681 -8.0848 9.0716 3.0989]}
{[ 45]}

If you use a batch of inputs, `gradient`

uses the whole input
sequence (in this case nine steps), and all the gradients with respect to the
independent batch dimensions (in this case five) are added together. Therefore, the
returned gradient always has the same size as the output from `getLearnableParameters`

.

### Accelerate Gradient Computation for a Discrete Categorical Actor

Create observation and action specification objects (or
alternatively use `getObservationInfo`

and
`getActionInfo`

to extract the specification objects from an
environment). For this example, define an observation space with two channels. The first
channel carries an observation from a continuous four-dimensional space. The second
carries a discrete scalar observation that can be either zero or one. Finally, the
action space consist of a scalar that can be `-1`

,
`0`

, or `1`

.

obsInfo = [rlNumericSpec([4 1]) rlFiniteSetSpec([0 1])]; actInfo = rlFiniteSetSpec([-1 0 1]);

Create a deep neural network to be used as approximation model within the actor. The
output layer must have three elements, each one expressing the value of executing the
corresponding action, given the observation. To create a recurrent neural network, use
`sequenceInputLayer`

as the input layer and include an
`lstmLayer`

as one of the other network layers.

% Define paths inPath1 = [ sequenceInputLayer(prod(obsInfo(1).Dimension)) fullyConnectedLayer(prod(actInfo.Dimension),Name="fc1") ]; inPath2 = [ sequenceInputLayer(prod(obsInfo(2).Dimension)) fullyConnectedLayer(prod(actInfo.Dimension),Name="fc2") ]; % Concatenate previous paths outputs along first dimension jointPath = [ concatenationLayer(1,2,Name="cct") tanhLayer lstmLayer(8,OutputMode="sequence") fullyConnectedLayer( ... prod(numel(actInfo.Elements)), ... Name="jntfc") ]; % Assemble dlnetwork object net = dlnetwork; net = addLayers(net,inPath1); net = addLayers(net,inPath2); net = addLayers(net,jointPath); % Connect layers net = connectLayers(net,"fc1","cct/in1"); net = connectLayers(net,"fc2","cct/in2"); % Plot network plot(net)

```
% initialize network and display the number of weights.
net = initialize(net);
summary(net)
```

Initialized: true Number of learnables: 386 Inputs: 1 'sequenceinput' Sequence input with 4 dimensions 2 'sequenceinput_1' Sequence input with 1 dimensions

Since each element of the output layer must represent the probability of executing
one of the possible actions the software automatically adds a
`softmaxLayer`

as a final output layer if you do not specify it
explicitly.

Create the actor with `rlDiscreteCategoricalActor`

, using the
network and the observations and action specification objects. When the network has
multiple input layers, they are automatically associated with the environment
observation channels according to the dimension specifications in
`obsInfo`

.

actor = rlDiscreteCategoricalActor(net, obsInfo, actInfo);

To return a vector of probabilities for each possible action, use
`evaluate`

.

[prob,state] = evaluate(actor, ... { rand(obsInfo(1).Dimension) , ... rand(obsInfo(2).Dimension) }); prob{1}

`ans = `*3x1 single column vector*
0.3403
0.3114
0.3483

To return an action sampled from the distribution, use
`getAction`

.

act = getAction(actor, ... { rand(obsInfo(1).Dimension) , ... rand(obsInfo(2).Dimension) }); act{1}

ans = 1

Set the option to accelerate the gradient computations.

actor = accelerate(actor,true);

Each array within a cell contains the gradient of the sum of the outputs with respect to a group of parameters.

grp_batch = gradient(actor,"output-parameters", ... { rand([obsInfo(1).Dimension 5 9]) , ... rand([obsInfo(2).Dimension 5 9])} )

`grp_batch=`*9×1 cell array*
{[-3.1996e-09 -4.5687e-09 -4.4820e-09 -4.6439e-09]}
{[ -1.1544e-08]}
{[ -1.1321e-08]}
{[ -2.8436e-08]}
{32x2 single }
{32x8 single }
{32x1 single }
{ 3x8 single }
{ 3x1 single }

If you use a batch of inputs, the `gradient`

uses the whole input
sequence (in this case nine steps), and all the gradients with respect to the
independent batch dimensions (in this case five) are added together. Therefore, the
returned gradient always has the same size as the output from `getLearnableParameters`

.

## Input Arguments

`oldAppx`

— Function approximator object

function approximator object

Function approximator object, specified as one of the following:

`rlValueFunction`

object — Value function critic`rlQValueFunction`

object — Q-value function critic`rlVectorQValueFunction`

object — Multi-output Q-value function critic with a discrete action space`rlContinuousDeterministicActor`

object — Deterministic policy actor with a continuous action space`rlDiscreteCategoricalActor`

— Stochastic policy actor with a discrete action space`rlContinuousGaussianActor`

object — Stochastic policy actor with a continuous action space`rlContinuousDeterministicTransitionFunction`

object — Continuous deterministic transition function for a model based agent`rlContinuousGaussianTransitionFunction`

object — Continuous Gaussian transition function for a model based agent`rlContinuousDeterministicRewardFunction`

object — Continuous deterministic reward function for a model based agent`rlContinuousGaussianRewardFunction`

object — Continuous Gaussian reward function for a model based agent.`rlIsDoneFunction`

object — Is-done function for a model based agent.

`useAcceleration`

— Option to use acceleration for gradient computations

`false`

(default) | `true`

Option to use acceleration for gradient computations, specified as a logical value.
When `useAcceleration`

is `true`

, the gradient
computations are accelerated by optimizing and caching some inputs needed by the
automatic-differentiation computation graph. For more information, see Deep Learning Function Acceleration for Custom Training Loops.

## Output Arguments

`newAppx`

— Actor or critic

approximator object

New actor or critic, returned as an approximator object with the same type as
`oldAppx`

but with the gradient acceleration option set to
`useAcceleration`

.

## Version History

**Introduced in R2022a**

### R2024a: `accelerate`

is not recommended

`accelerate`

is no longer recommended.

Instead of using `accelerate`

to accelerate the gradient computation of a function approximator
object, use `dlaccelerate`

on
your loss function. Then use `dlfeval`

on the
`AcceleratedFunction`

object returned by `dlaccelerate`

.

This workflow is shown in the following table.

`accelerate` : Not Recommended | `dlaccelerate` : Recommended |
---|---|

actor = accelerate(actor,true); g = gradient(actor,@customLoss,u); g{1} function loss = customLoss(y,varargin) loss = sum(y{1}.^2); | f = dlaccelerate(@customLoss); g = dlfeval(@customLoss,actor,dlarray(u)); g{1} function g = customLoss(actor,u) y = evaluate(actor,u); loss = sum(y{1}.^2); g = dlgradient(loss,actor.Learnables); |

For more information, see also gradient is not recommended.

For more information on using `dlarray`

objects for custom deep learning
training loops, see `dlfeval`

, `AcceleratedFunction`

, `dlaccelerate`

.

For an example, see Train Reinforcement Learning Policy Using Custom Training Loop and Custom Training Loop with Simulink Action Noise.

## See Also

### Functions

### Objects

`AcceleratedFunction`

|`rlValueFunction`

|`rlQValueFunction`

|`rlVectorQValueFunction`

|`rlContinuousDeterministicActor`

|`rlDiscreteCategoricalActor`

|`rlContinuousGaussianActor`

|`rlContinuousDeterministicTransitionFunction`

|`rlContinuousGaussianTransitionFunction`

|`rlContinuousDeterministicRewardFunction`

|`rlContinuousGaussianRewardFunction`

|`rlIsDoneFunction`

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