Recent Advances in Reinforcement Learning

Vidyasagar, Mathukumalli (2020) Recent Advances in Reinforcement Learning. In: 2020 American Control Conference, ACC 2020, 1-3 July 2020, Denver.

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Abstract

In this paper, we give a brief review of Markov Decision Processes (MDPs), and how Reinforcement Learning (RL) can be viewed as MDP where the parameters are unknown. Specific topics discussed include the Bellman equation and the Bellman operator, and value and policy iterations for MDPs, together with recent "empirical" approaches to solving the Bellman equation and applying the Bellman iteration. In addition to the well-established method of Q-learning, we also discuss the more recent approach known as Zap Q-learning. © 2020 AACC.

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IITH Creators:
IITH CreatorsORCiD
Vidyasagar, Mathukumallihttps://orcid.org/0000-0003-1057-1942
Item Type: Conference or Workshop Item (Paper)
Additional Information: M. Vidyasagar is a National Science Chair, and is with the Indian Institute of Technology Hyderabad, India, This research is supported by the Science and Engineering Research Board (SERB), Government of India. Email: m.vidyasagar@iith.ac.in.
Uncontrolled Keywords: Bellman equations; Markov Decision Processes; Policy iteration; Q-learning
Subjects: Electrical Engineering
Divisions: Department of Electrical Engineering
Depositing User: . LibTrainee 2021
Date Deposited: 23 Nov 2022 09:11
Last Modified: 23 Nov 2022 09:11
URI: http://raiith.iith.ac.in/id/eprint/11313
Publisher URL: https://doi.org/10.23919/ACC45564.2020.9147512
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