# evaluate

Evaluate function approximator object given observation (or observation-action) input data

*Since R2022a*

## Syntax

## Description

`___ = evaluate(___,UseForward=`

allows you to explicitly call a forward pass when computing gradients.`useForward`

)

## Examples

### Evaluate Function Approximator Object

This example shows you how to evaluate a function approximator object (that is, an actor or a critic). For this example, the function approximator object is a discrete categorical actor and you evaluate it given some observation data, obtaining in return the action probability distribution and the updated network state.

Load the same environment used in Train PG Agent to Balance Discrete Cart-Pole System, and obtain the observation and action specifications.

```
env = rlPredefinedEnv("CartPole-Discrete");
obsInfo = getObservationInfo(env)
```

obsInfo = rlNumericSpec with properties: LowerLimit: -Inf UpperLimit: Inf Name: "CartPole States" Description: "x, dx, theta, dtheta" Dimension: [4 1] DataType: "double"

actInfo = getActionInfo(env)

actInfo = rlFiniteSetSpec with properties: Elements: [-10 10] Name: "CartPole Action" Description: [0x0 string] Dimension: [1 1] DataType: "double"

To approximate the policy within the actor, use a recurrent deep neural network. Define the network as an array of layer objects. Get the dimensions of the observation space and the number of possible actions directly from the environment specification objects.

```
net = [
sequenceInputLayer(prod(obsInfo.Dimension))
fullyConnectedLayer(8)
reluLayer
lstmLayer(8,OutputMode="sequence")
fullyConnectedLayer(numel(actInfo.Elements))
];
```

Convert the network to a `dlnetwork`

object and display the number of weights.

net = dlnetwork(net); summary(net)

Initialized: true Number of learnables: 602 Inputs: 1 'sequenceinput' Sequence input with 4 dimensions

Create a stochastic actor representation for the network.

actor = rlDiscreteCategoricalActor(net,obsInfo,actInfo);

Use `evaluate`

to return the probability of each of the two possible actions. Note that the type of the returned numbers is `single`

, not `double`

.

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

`ans = `*2x1 single column vector*
0.4847
0.5153

Since a recurrent neural network is used for the actor, the second output argument, representing the updated state of the neural network, is not empty. In this case, it contains the updated (cell and hidden) states for the eight units of the `lstm`

layer used in the network.

state{:}

`ans = `*8x1 single column vector*
-0.0833
0.0619
-0.0066
-0.0651
0.0714
-0.0957
0.0614
-0.0326

`ans = `*8x1 single column vector*
-0.1367
0.1142
-0.0158
-0.1820
0.1305
-0.1779
0.0947
-0.0833

You can use dot notation to extract and set the current state of the recurrent neural network in the actor.

actor.State

`ans=`*2×1 cell array*
{8x1 dlarray}
{8x1 dlarray}

actor.State = { dlarray(-0.1*rand(8,1)) dlarray(0.1*rand(8,1)) };

You can obtain action probabilities and updated states for a batch of observations. For example, use a batch of five independent observations.

obsBatch = reshape(1:20,4,1,5,1); [prob,state] = evaluate(actor,{obsBatch})

`prob = `*1x1 cell array*
{2x5 single}

`state=`*2×1 cell array*
{8x5 single}
{8x5 single}

The output arguments contain action probabilities and updated states for each observation in the batch.

Note that the actor treats observation data along the batch length dimension independently, not sequentially.

prob{1}

`ans = `*2x5 single matrix*
0.5303 0.5911 0.6083 0.6158 0.6190
0.4697 0.4089 0.3917 0.3842 0.3810

prob = evaluate(actor,{obsBatch(:,:,[5 4 3 1 2])}); prob{1}

`ans = `*2x5 single matrix*
0.6190 0.6158 0.6083 0.5303 0.5911
0.3810 0.3842 0.3917 0.4697 0.4089

To evaluate the actor using sequential observations, use the sequence length (time) dimension. For example, obtain action probabilities for five independent sequences, each one made of nine sequential observations.

```
[prob,state] = evaluate(actor, ...
{rand([obsInfo.Dimension 5 9])})
```

`prob = `*1x1 cell array*
{2x5x9 single}

`state=`*2×1 cell array*
{8x5 single}
{8x5 single}

The first output argument contains a vector of two probabilities (first dimension) for each element of the observation batch (second dimension) and for each time element of the sequence length (third dimension).

The second output argument contains two vectors of final states for each observation batch (that is, the network maintains a separate state history for each observation batch).

Display the probability of the second action, after the seventh sequential observation in the fourth independent batch.

prob{1}(2,4,7)

`ans = `*single*
0.5653

For more information on input and output format for recurrent neural networks, see the Algorithms section of `lstmLayer`

.

## Input Arguments

`fcnAppx`

— 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

`inData`

— Input data for function approximator

cell array

Input data for the function approximator, specified as a cell array with as many
elements as the number of input channels of `fcnAppx`

. In the
following section, the number of observation channels is indicated by
*N _{O}*.

If

`fcnAppx`

is an`rlQValueFunction`

, an`rlContinuousDeterministicTransitionFunction`

or an`rlContinuousGaussianTransitionFunction`

object, then each of the first*N*elements of_{O}`inData`

must be a matrix representing the current observation from the corresponding observation channel. They must be followed by a final matrix representing the action.If

`fcnAppx`

is a function approximator object representing an actor or critic (but not an`rlQValueFunction`

object),`inData`

must contain*N*elements, each one a matrix representing the current observation from the corresponding observation channel._{O}If

`fcnAppx`

is an`rlContinuousDeterministicRewardFunction`

, an`rlContinuousGaussianRewardFunction`

, or an`rlIsDoneFunction`

object, then each of the first*N*elements of_{O}`inData`

must be a matrix representing the current observation from the corresponding observation channel. They must be followed by a matrix representing the action, and finally by*N*elements, each one being a matrix representing the next observation from the corresponding observation channel._{O}

Each element of `inData`

must be a matrix of dimension
*M _{C}*-by-

*L*-by-

_{B}*L*, where:

_{S}*M*corresponds to the dimensions of the associated input channel._{C}*L*is the batch size. To specify a single observation, set_{B}*L*= 1. To specify a batch of (independent) inputs, specify_{B}*L*> 1. If_{B}`inData`

has multiple elements, then*L*must be the same for all elements of_{B}`inData`

.*L*specifies the sequence length (length of the sequence of inputs along the time dimension) for recurrent neural network. If_{S}`fcnAppx`

does not use a recurrent neural network (which is the case for environment function approximators, as they do not support recurrent neural networks), then*L*= 1. If_{S}`inData`

has multiple elements, then*L*must be the same for all elements of_{S}`inData`

.

For more information on input and output formats for recurrent neural networks, see
the Algorithms section of `lstmLayer`

.

**Example: **`{rand(8,3,64,1),rand(4,1,64,1),rand(2,1,64,1)}`

`useForward`

— Option to use parallel training

`false`

(default) | `true`

## Output Arguments

`outData`

— Output data from evaluation of function approximator object

cell array

Output data from the evaluation of the function approximator object, returned as a
cell array. The size and contents of `outData`

depend on the type of
object you use for `fcnAppx`

, and are shown in the following list.
Here, *N _{O}* is the number of observation
channels.

`rlContinuousDeterministicTransitionFunction`

-*N*matrices, each one representing the predicted observation from the corresponding observation channel._{O}`rlContinuousGaussianTransitionFunction`

-*N*matrices representing the mean value of the predicted observation for the corresponding observation channel, followed by_{O}*N*matrices representing the standard deviation of the predicted observation for the corresponding observation channel._{O}`rlContinuousGaussianActor`

- Two matrices representing the mean value and standard deviation of the action, respectively.`rlDiscreteCategoricalActor`

- A matrix with the probabilities of each action.`rlContinuousDeterministicActor`

A matrix with the action.`rlVectorQValueFunction`

- A matrix with the values of each possible action.`rlQValueFunction`

- A matrix with the value of the action.`rlValueFunction`

- A matrix with the value of the current observation.`rlContinuousDeterministicRewardFunction`

- A matrix with the predicted reward as a function of current observation, action, and next observation following the action.`rlContinuousGaussianRewardFunction`

- Two matrices representing the mean value and standard deviation, respectively, of the predicted reward as a function of current observation, action, and next observation following the action.`rlIsDoneFunction`

- A vector with the probabilities of the predicted termination status. Termination probabilities range from`0`

(no termination predicted) or`1`

(termination predicted), and depend (in the most general case) on the values of observation, action, and next observation following the action.

Each element of `outData`

is a matrix of dimensions
*D*-by-*L _{B}*-by-

*L*, where:

_{S}*D*is the vector of dimensions of the corresponding output channel of`fcnAppx`

. Depending on the type of approximator function, this channel can carry a predicted observation (or its mean value or standard deviation), an action (or its mean value or standard deviation), the value (or values) of an observation (or observation-action couple), a predicted reward, or a predicted termination status.*L*is the batch size (length of a batch of independent inputs)._{B}*L*is the sequence length (length of the sequence of inputs along the time dimension) for a recurrent neural network. If_{S}`fcnAppx`

does not use a recurrent neural network (which is the case for environment function approximators, as they do not support recurrent neural networks), then*L*= 1._{S}

**Note**

If `fcnAppx`

is a critic, then `evaluate`

behaves identically to `getValue`

except that it returns results inside a single-cell array. If
`fcnAppx`

is an `rlContinuousDeterministicActor`

actor, then `evaluate`

behaves identically to `getAction`

. If
`fcnAppx`

is a stochastic actor such as an `rlDiscreteCategoricalActor`

or `rlContinuousGaussianActor`

, then `evaluate`

returns the
action probability distribution, while `getAction`

returns a sample action. Specifically, for an `rlDiscreteCategoricalActor`

actor object, `evaluate`

returns the probability of each possible action. For an `rlContinuousGaussianActor`

actor object, `evaluate`

returns the mean and standard deviation of the Gaussian distribution. For these kinds
of actors, see also the note in `getAction`

regarding the enforcement of constraints set by the action specification.

**Note**

If `fcnAppx`

is an `rlContinuousDeterministicRewardFunction`

object, then
`evaluate`

behaves identically to `predict`

except that it returns results inside a single-cell array. If
`fcnAppx`

is an `rlContinuousDeterministicTransitionFunction`

object, then
`evaluate`

behaves identically to `predict`

. If
`fcnAppx`

is an `rlContinuousGaussianTransitionFunction`

object, then
`evaluate`

returns the mean value and standard deviation the
observation probability distribution, while `predict`

returns an observation sampled from this distribution. Similarly, for an `rlContinuousGaussianRewardFunction`

object, `evaluate`

returns the mean value and standard deviation the reward probability distribution,
while `predict`

returns a reward sampled from this distribution. Finally, if
`fcnAppx`

is an `rlIsDoneFunction`

object, then `evaluate`

returns the
probabilities of the termination status being false or true, respectively, while
`predict`

returns a predicted termination status sampled with
these probabilities.

`nextState`

— Updated state of function approximator object

cell array

Next state of the function approximator object, returned as a cell array. If
`fcnAppx`

does not use a recurrent neural network (which is the
case for environment function approximators), then `nextState`

is an
empty cell array.

You can set the state of the approximator to `state`

using dot
notation. For example:

critic.State = state;

## Tips

When the elements of the cell array in `inData`

are
`dlarray`

objects, the elements of the cell array returned in
`outData`

are also `dlarray`

objects. This allows
`evaluate`

to be used with automatic differentiation.

Specifically, you can write a custom loss function that directly uses
`evaluate`

and `dlgradient`

within
it, and then use `dlfeval`

and
`dlaccelerate`

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

## Version History

**Introduced in R2022a**

## See Also

### Functions

`runEpisode`

|`update`

|`rlOptimizer`

|`syncParameters`

|`getValue`

|`getAction`

|`getMaxQValue`

|`getLearnableParameters`

|`setLearnableParameters`

|`predict`

### Objects

`rlValueFunction`

|`rlQValueFunction`

|`rlVectorQValueFunction`

|`rlContinuousDeterministicActor`

|`rlDiscreteCategoricalActor`

|`rlContinuousGaussianActor`

|`rlContinuousDeterministicTransitionFunction`

|`rlContinuousGaussianTransitionFunction`

|`rlContinuousDeterministicRewardFunction`

|`rlContinuousGaussianRewardFunction`

|`rlIsDoneFunction`

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