Title

On Experience Replay

Date of Award

2019

Document Type

Thesis

Degree Name

Master of Science in Data Science

Department

Information Systems & Computer Science

First Advisor

John Paul C. Vergara, PhD

Abstract

Reinforcement learning has proved to be of great utility in myriad set- tings; execution, however, may be costly due to sampling inefficiency. Experi- ence replay is used in reinforcement learning for efficient learning by recalling past experiences. While relative merits are unclear, several experience replay algorithms, namely, combined experience replay, hindsight experience replay, and prioritized experience replay, have been crafted. In this study, one surveys the existing methods and proposes hybrid replay algorithms – with hindsight and combined experience replay and with hindsight and prioritized experience replay. A comparison of the variations of experience replay incorporated into a reinforcement learning algorithm is also proffered towards an attempt to create a novel replay technique based on prioritization variants. To close, the case of multi-agent learning is considered.

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