What is the difference between model-free and model-based reinforcement learning, and how do each of these approaches handle the decision-making process?
In the domain of reinforcement learning (RL), there exists a fundamental distinction between model-free and model-based approaches, each offering unique methodologies for the decision-making process. Model-free reinforcement learning refers to methods that learn policies or value functions directly from interactions with the environment without constructing an explicit model of the environment's dynamics. This approach relies
- Published in Artificial Intelligence, EITC/AI/ARL Advanced Reinforcement Learning, Deep reinforcement learning, Planning and models, Examination review
How does the Q-learning algorithm work?
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