How does the Q-learning algorithm work?
Monday, 03 June 2024
by asadeghp
Q-learning is a type of reinforcement learning algorithm that was first introduced by Watkins in 1989. It is designed to find the optimal action-selection policy for any given finite Markov decision process (MDP). The goal of Q-learning is to learn the quality of actions, which is represented by the Q-values. These Q-values are used to
- Published in Artificial Intelligence, EITC/AI/ARL Advanced Reinforcement Learning, Introduction, Introduction to reinforcement learning
Tagged under:
Artificial Intelligence, Bellman Equation, Model-Free, Optimal Policy, Q-learning, Reinforcement Learning