NDLI: Symbol generation and feature selection for reinforcement learning agents using affordances and U-Trees

Content Provider	IEEE Xplore Digital Library
Author	Oladell, M. Huber, M.
Copyright Year	2012
Description	Author affiliation: Department of Computer Science and Engineering, University of Texas at Arlington, USA (Oladell, M.; Huber, M.)
Abstract	One of the challenges for artificial agents is managing the complexity of their environment and task domain as they learn increasingly difficult tasks. This is especially true of agents that are grounded in the physical world, which contains a vast number of features and potentially very complex dynamics. A scalable solution to this problem in terms of forming, managing, and re-using compact, grounded representations in order to address the state explosion problem is thus a prerequisite of physically grounded, agent-based systems that can apply their past experience to new tasks and communicate that experience with other agents. To achieve this, it is essential that agents can form conceptual features that are relevant for and re-usable in their task domain without outside intervention and that these agents can effectively focus their attention on only the relevant features and concepts for the task at hand. This paper presents a framework for managing state complexity by automatically constructing abstract, symbolic features which encode important, task and domain-relevant properties and partition the raw feature space such that the agent need only consider a compressed view of the environment when learning new tasks. To exploit this, the framework during learning of new tasks uses U-Trees to construct minimal feature sets and thus compact state representations for these new tasks, allowing for potentially significant improvements in learning times.
Starting Page	657
Ending Page	662
File Size	234117
Page Count	6
File Format	PDF
ISBN	9781467317139
e-ISBN	9781467317146
e-ISBN	9781467317122
DOI	10.1109/ICSMC.2012.6377801
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-10-14
Publisher Place	Korea (South)
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Abstracts Complexity theory Context Robots Training data Grippers Learning Reinforcement Learning State Abstraction Symbol Grounding Affordance-Based Learning
Content Type	Text
Resource Type	Article

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
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