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NCTS Seminar on Reinforcement Learning
 
20:00 - 21:00, March 8, 2021 (Monday)
SA223, Science Building I, NYCU
(交通大學科學一館 223室)
Introduction of Reinforcement Learning
Kuok Tong Ng (National Yang Ming Chiao Tung University)

Abstract:
Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. RL is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning. RL differs from supervised learning in not needing labelled input/output pairs be presented, and in not needing sub-optimal actions to be explicitly corrected. Instead the focus is on finding a balance between exploration (of uncharted territory) and exploitation (of current knowledge). The environment is typically stated in the form of a Markov decision process (MDP), because many RL algorithms for this context use dynamic programming techniques. The main difference between the classical dynamic programming methods and RL algorithms is that the latter do not assume knowledge of an exact mathematical model of the MDP and they target large MDPs where exact methods become infeasible. In this talk, we will briefly introduce the following topics: Markov Decision Process (MDP), Bellman Equations of value functions, Q-learning and Deep Q-learning (DQN).


 

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