Value table or Q table
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.
rlRepresentation | Model representation for reinforcement learning agents |