# rlVectorQValueFunction

Vector Q-value function approximator for reinforcement learning agents

*Since R2022a*

## Description

This object implements a vector Q-value function approximator that you can use as
a critic with a discrete action space for a reinforcement learning agent. A vector Q-value
function is a mapping from an environment observation to a vector in which each element
represents the expected discounted cumulative long-term reward when an agent starts from the
state corresponding to the given observation and executes the action corresponding to the
element number (and follows a given policy afterwards). A vector Q-value function critic
therefore needs only the observation as input. After you create an
`rlVectorQValueFunction`

critic, use it to create an agent such as `rlQAgent`

, `rlDQNAgent`

, `rlSARSAAgent`

. For more
information on creating representations, see Create Policies and Value Functions.

## Creation

### Syntax

### Description

creates the `critic`

= rlVectorQValueFunction(`net`

,`observationInfo`

,`actionInfo`

)*multi-output* Q-value function
`critic`

with a *discrete action space*. Here,
`net`

is the deep neural network used as an approximation model,
and must have only the observations as input and a single output layer having as many
elements as the number of possible discrete actions. The network input layers are
automatically associated with the environment observation channels according to the
dimension specifications in `observationInfo`

. This function sets the
`ObservationInfo`

and `ActionInfo`

properties of
`critic`

to the `observationInfo`

and
`actionInfo`

input arguments, respectively.

specifies the names of the network input layers to be associated with the environment
observation channels. The function assigns, in sequential order, each environment
observation channel specified in `critic`

= rlVectorQValueFunction(`net`

,`observationInfo`

,ObservationInputNames=`netObsNames`

)`observationInfo`

to the layer
specified by the corresponding name in the string array
`netObsNames`

. Therefore, the network input layers, ordered as the
names in `netObsNames`

, must have the same data type and dimensions
as the observation channels, as ordered in `observationInfo`

.

creates the `critic`

= rlVectorQValueFunction({`basisFcn`

,`W0`

},`observationInfo`

,`actionInfo`

)*multi-output* Q-value function
`critic`

with a *discrete action space* using a
custom basis function as underlying approximation model. The first input argument is a
two-element cell array whose first element is the handle `basisFcn`

to a custom basis function and whose second element is the initial weight matrix
`W0`

. Here the basis function must have only the observations as
inputs, and `W0`

must have as many columns as the number of possible
actions. The function sets the ObservationInfo
and ActionInfo
properties of `critic`

to the input arguments
`observationInfo`

and `actionInfo`

,
respectively.

specifies the device used to perform computations for the `critic`

= rlVectorQValueFunction(___,UseDevice=`useDevice`

)`critic`

object, and sets the `UseDevice`

property of
`critic`

to the `useDevice`

input argument. You
can use this syntax with any of the previous input-argument combinations.

### Input Arguments

## Properties

## Object Functions

`rlDQNAgent` | Deep Q-network (DQN) reinforcement learning agent |

`rlQAgent` | Q-learning reinforcement learning agent |

`rlSARSAAgent` | SARSA reinforcement learning agent |

`getValue` | Obtain estimated value from a critic given environment observations and actions |

`getMaxQValue` | Obtain maximum estimated value over all possible actions from a Q-value function critic with discrete action space, given environment observations |

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

`gradient` | Evaluate gradient of function approximator object given observation and action input data |

`accelerate` | Option to accelerate computation of gradient for approximator object based on neural network |

`getLearnableParameters` | Obtain learnable parameter values from agent, function approximator, or policy object |

`setLearnableParameters` | Set learnable parameter values of agent, function approximator, or policy object |

`setModel` | Set function approximation model for actor or critic |

`getModel` | Get function approximator model from actor or critic |

## Examples

## Version History

**Introduced in R2022a**