 |
Reinforcement learning is a method in which agents and environments interact with each other to learn.
This proceeds with learning through the results of the action in a specific state.
It aims to find its optimal policy and calculates the reward.
It is characterized by being defined in the 5-tuple form of the Markov Decision Process.
5-tuple includes elements of state, action, transition probability, reward, and Discount Factor.
The figure below is an overall structural diagram of reward calculation and reinforcement learning for state and action.
|
IEEE/ICACT20220065 Slide.05
[Big Slide]