Self-Improving Generative Adversarial Reinforcement Learning

Yang Liu, Yifeng Zeng, Yingke Chen, Jing Tang

Research output: Chapter in Book/Report/Conference proceedingConference contribution


The lack of data efficiency and stability is one of the main challenges in end-to-end model free reinforcement learning (RL) methods. Recent researches solve the problem resort to supervised learning methods by utilizing human expert demonstrations, e.g. imitation learning. In this paper we present a novel framework which builds a self-improving process upon a policy improvement operator, which is used as a black box such that it has multiple implementation options for various applications. An agent is trained to iteratively imitate behaviors that are generated by the operator. Hence the agent can learn by itself without domain knowledge from human. We employ generative adversarial networks (GAN) to implement the imitation module in the new framework. We evaluate the framework performance over multiple application domains and provide comparison results in support. © 2019 International Foundation for Autonomous Agents and Multiagent Systems ( All rights reserved.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems, AAMAS
ISBN (Print)9781510892002
Publication statusPublished - 15 May 2019
EventAAMAS 2019 - Concordia University, Montreal, Canada
Duration: 13 May 201917 May 2019

Publication series

Name ACM International Conference on Autonomous Agents and Multiagent Systems. Proceedings
PublisherAssociation for Computing Machinery
ISSN (Print)1548-8403


ConferenceAAMAS 2019
Internet address


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