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
Table— Reward table
Reward table, returned as an array. When
Table is a:
Value table, it contains NO rows, where NO is the number of finite observation values.
Q table, it contains NO rows and NA columns, where NA is the number of possible finite actions.
|Model representation for reinforcement learning agents|