Tabular Q-learning: Revision history

19 March 2023

• curprev 06:2406:24, 19 March 2023‎ Walle talk contribs‎ 3,669 bytes +3,669‎ Created page with "{{see also|Machine learning terms}} ==Introduction== Tabular Q-learning is a fundamental reinforcement learning algorithm used in the field of machine learning. It is a value-based approach that helps agents learn optimal policies through interaction with their environment. The algorithm aims to estimate the expected cumulative reward or ''value'' for each state-action pair in a discrete environment. ==Q-learning Algorithm== Q-learning is a model-free, off-polic..."