rlTable
Value table or Q table
Description
Value tables and Q-tables are one of the approximation models that can be used within value functions and Q-value functions, respectively. 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 approximator using an rlTable
object, use an
rlValueFunction
,
rlQValueFunction
, or
rlVectorQValueFunction
object.
Creation
Description
Input Arguments
Properties
Object Functions
rlValueFunction | Value function approximator object for reinforcement learning agents |
rlQValueFunction | Q-Value function approximator with a continuous or discrete action space reinforcement learning agents |
rlVectorQValueFunction | Vector Q-value function approximator with hybrid or discrete action space for reinforcement learning agents |
Examples
Version History
Introduced in R2019a