What is the difference between model-free and model-based reinforcement learning, and how do each of these approaches handle the decision-making process?
Tuesday, 11 June 2024
by EITCA Academy
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
Tagged under:
Artificial Intelligence, Model-Based, Model-Free, Policy Gradient, Q-learning, Reinforcement Learning