Interpolated experience replay for continuous environments

  • The concept of Experience Replay is a crucial element in Deep Reinforcement Learning algorithms of the DQN family. The basic approach reuses stored experiences to, amongst other reasons, overcome the problem of catastrophic forgetting and as a result stabilize learning. However, only experiences that the learner observed in the past are used for updates. We anticipate that these experiences posses additional valuable information about the underlying problem that just needs to be extracted in the right way. To achieve this, we present the Interpolated Experience Replay technique that leverages stored experiences to create new, synthetic ones by means of interpolation. A previous proposed concept for discrete-state environments is extended to work in continuous problem spaces. We evaluate our approach on the MountainCar benchmark environment and demonstrate its promising potential.

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Author:Wenzel Baron Pilar von PilchauORCiDGND, Anthony SteinGND, Jörg HähnerORCiDGND
Frontdoor URL
Parent Title (English):Proceedings of the 14th International Joint Conference on Computational Intelligence, October 24-26, 2022, in Valletta, Malta - Volume 1: NCTA
Place of publication:Setúbal
Editor:Thomas Bäck, Bas van Stein, Christian Wagner, Jonathan Garibaldi, H. K. Lam, Marie Cottrell, Faiyaz Doctor, Joaquim Filipe, Kevin Warwick, Janusz Kacprzyk
Type:Part of a Book
Year of first Publication:2022
Publishing Institution:Universität Augsburg
Release Date:2022/12/06
First Page:237
Last Page:248
Institutes:Fakultät für Angewandte Informatik
Fakultät für Angewandte Informatik / Institut für Informatik
Fakultät für Angewandte Informatik / Institut für Informatik / Lehrstuhl für Organic Computing
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke / 00 Informatik, Wissen, Systeme / 004 Datenverarbeitung; Informatik
Licence (German):CC-BY-NC-ND 4.0: Creative Commons: Namensnennung - Nicht kommerziell - Keine Bearbeitung (mit Print on Demand)