rlTable

Value table or Q table

Description

You can create value tables and Q tables to represent critic networks for reinforcement learning. Value tables store rewards for a finite set of observations. Q tables store rewards for corresponding finite observation-action pairs.

To create a value function representation using an rlTable object, use the rlRepresentation function.

Creation

Syntax

T = rlTable(obsinfo)

T = rlTable(obsinfo,actinfo)

Description

T = rlTable(obsinfo) creates a value table for the given discrete observations.

T = rlTable(obsinfo,actinfo) creates a Q table for the given discrete observations and actions.

Input Arguments

obsinfo — Observation specification

rlFiniteSetSpec object

Observation specification, specified as an rlFiniteSetSpec object.

actinfo — Action specification

rlFiniteSetSpec object

Action specification, specified as an rlFiniteSetSpec object.

Properties

Table — Reward table

array

Reward table, returned as an array. When Table is a:

• Value table, it contains N_O rows, where N_O is the number of finite observation values.

• Q table, it contains N_O rows and N_A columns, where N_A is the number of possible finite actions.

Object Functions

rlRepresentation

Model representation for reinforcement learning agents