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Title: Goal modelling for deep reinforcement learning agents
Authors: Leung, Jonathan
Shen, Zhiqi
Zeng, Zhiwei
Miao, Chunyan
Keywords: Engineering::Computer science and engineering
Issue Date: 2021
Source: Leung, J., Shen, Z., Zeng, Z. & Miao, C. (2021). Goal modelling for deep reinforcement learning agents. Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2021), 12975, 271-286.
Project: NRF-NRFI05-2019-0002
Abstract: Goals provide a high-level abstraction of an agent’s objectives and guide its behavior in complex environments. As agents become more intelligent, it is necessary to ensure that the agent’s goals are aligned with the goals of the agent designers to avoid unexpected or unwanted agent behavior. In this work, we propose using Goal Net, a goal-oriented agent modelling methodology, as a way for agent designers to incorporate their prior knowledge regarding the subgoals an agent needs to achieve in order to accomplish an overall goal. This knowledge is used to guide the agent’s learning process to train it to achieve goals in dynamic environments where its goal may change between episodes. We propose a model that integrates a Goal Net model and hierarchical reinforcement learning. A high-level goal selection policy selects goals according to a given Goal Net model and a low-level action selection policy selects actions based on the selected goal, both of which use deep neural networks to enable learning in complex, high-dimensional environments. The experiments demonstrate that our method is more sample efficient and can obtain higher average rewards than other related methods that incorporate prior human knowledge in similar ways.
ISBN: 9783030864859
ISSN: 0302-9743
DOI: 10.1007/978-3-030-86486-6_17
Rights: © 2021 Springer Nature Switzerland AG. All rights reserved. This paper was published in Proceedings of Joint European Conference on Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2021) and is made available with permission of Springer Nature Switzerland AG.
Fulltext Permission: embargo_20220917
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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