Stochastic actor representation for reinforcement learning agents

This object implements a function approximator to be used as a stochastic actor
within a reinforcement learning agent. A stochastic actor takes the observations as inputs and
returns a random action, thereby implementing a stochastic policy with a specific probability
distribution. After you create an `rlStochasticActorRepresentation`

object, use
it to create a suitable agent, such as an `rlACAgent`

or `rlPGAgent`

agent. For
more information on creating representations, see Create Policy and Value Function Representations.

creates a stochastic actor with a discrete action space, using the deep neural network
`discActor`

= rlStochasticActorRepresentation(`net`

,`observationInfo`

,`discActionInfo`

,'Observation',`obsName`

)`net`

as function approximator. Here, the output layer of
`net`

must have as many elements as the number of possible discrete
actions. This syntax sets the ObservationInfo
and ActionInfo
properties of `discActor`

to the inputs
`observationInfo`

and `discActionInfo`

respectively. `obsName`

must contain the names of the input layers of
`net`

.

creates a discrete space stochastic actor using a custom basis function as underlying
approximator. The first input argument is a two-elements cell in which the first element
contains the handle `discActor`

= rlStochasticActorRepresentation({`basisFcn`

,`W0`

},`observationInfo`

,`actionInfo`

)`basisFcn`

to a custom basis function, and the
second element contains the initial weight matrix `W0`

. This syntax
sets the ObservationInfo
and ActionInfo
properties of `actor`

respectively to the inputs
`observationInfo`

and `actionInfo`

.

creates the discrete action space, stochastic actor `discActor`

= rlStochasticActorRepresentation(___,`options`

)`discActor`

using
the additional options set `options`

, which is an `rlRepresentationOptions`

object. This syntax sets the Options
property of `discActor`

to the `options`

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

creates a Gaussian stochastic actor with a continuous action space using the deep neural
network `contActor`

= rlStochasticActorRepresentation(`net`

,`observationInfo`

,`contActionInfo`

,'Observation',`obsName`

)`net`

as function approximator. Here, the output layer of
`net`

must have twice as many elements as the number of dimensions
of the continuous action space. This syntax sets the ObservationInfo
and ActionInfo
properties of `contActor`

to the inputs
`observationInfo`

and `contActionInfo`

respectively. `obsName`

must contain the names of the input layers of
`net`

.

`contActor`

does not enforce constraints set by the action
specification, therefore, when using this actor, you must enforce action space
constraints within the environment.

creates the continuous action space, Gaussian actor `contActor`

= rlStochasticActorRepresentation(___,`options`

)`contActor`

using
the additional `options`

option set, which is an `rlRepresentationOptions`

object. This syntax sets the Options
property of `contActor`

to the `options`

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

`rlACAgent` | Actor-critic reinforcement learning agent |

`rlPGAgent` | Policy gradient reinforcement learning agent |

`rlPPOAgent` | Proximal policy optimization reinforcement learning agent |

`getAction` | Obtain action from agent or actor representation given environment observations |