NDLI: Implementing autonomous shaping by critical states

Content Provider	IEEE Xplore Digital Library
Author	Jiong Song Zhao Jin
Copyright Year	2011
Description	Author affiliation: Yunnan Jiao Tong Vocational and technical College, Kunming, 650101, China (Jiong Song) \|\| Yunnan University, Kunming, 650091, China (Zhao Jin)
Abstract	Shaping is a powerful method for speeding up reinforcement learning, but the major drawback that shaping reward depends on external observer limits its application and requires significant effort. We implement an autonomous shaping reinforcement learning method by making agent can discover autonomously critical states from prior experience and use them to shape later learning. The critical state is a state that has high probability to exist in all these acyclic state trajectories that from the start state to the goal state, that means, if agent wants to reach the goal state, then it would have high likelihood to pass the critical states. So the critical states can be used to shape agent for reaching the goal state faster. The experiments on Maze problem show our method can significant improve agent's performance. The more important is we make agent can shape its later learning by its prior experience.
Starting Page	276
Ending Page	279
File Size	116347
Page Count	4
File Format	PDF
ISBN	9781424486229
e-ISBN	9781424486250
DOI	10.1109/ITAIC.2011.6030203
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-20
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Learning Training Learning systems Shape Shaping Machine learning Reinforcement learning Observers Prior experience Trajectory Critical State Speeding up learning
Content Type	Text
Resource Type	Article

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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