Basic Information
Autonomous Agents Operating in a Complex Environment Are Required To Function Under Challenging Conditions of Uncertainty, Resulting From Partial, Noisy and Delayed Information, From Lack of a World Model, And System Malfunction and Form Communication Bottlenecks. a Possible Approach to These Difficulties Combines Exploration and Exploitation. Roughly, Exploitation Involves Utilizing Prior Knowledge, Collected Through Activity Aimed at Achieving Required Gals, While Exploration Focuses On Searching For Modes of Operation With Potential Future Gains. The Optimal Balance Between Exploration and Exploitation Occupies a Basic Place in The The Areas of Optimal Control And Reinforcement Learning Since The Early 1960s, With Increasing Importance in Recent Years. in Spite of This, Except For Restricted Cases, There Is Limited Understanding of How to Balance The Two. In This Course We Will Characterize This Balance in Different Learning Systems, Aiming at The Extraction of General Principles, Opening The Door to The Development of Effective Exploration-exploitation Schemes in Challenging Problems in Machine Learning. Learning Outcomes# Understanding The Balance Between Exploration And Exploitation in Learning Stems, Understanding The Basic Theory For Simple Systems, Designing Effective an Exploration-exploitation Balance in More Complex Systems, Reading The Current Literature.