Inverse Reinforcement Learning with Leveraged Gaussian Processes
- Authors
- Lee, Kyungjae; Choi, Sungjoon; Oh, Songhwai
- Issue Date
- Oct-2016
- Publisher
- IEEE
- Citation
- 2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016), pp 3907 - 3912
- Pages
- 6
- Journal Title
- 2016 IEEE/RSJ INTERNATIONAL CONFERENCE ON INTELLIGENT ROBOTS AND SYSTEMS (IROS 2016)
- Start Page
- 3907
- End Page
- 3912
- URI
- https://scholarworks.bwise.kr/cau/handle/2019.sw.cau/59377
- DOI
- 10.1109/IROS.2016.7759575
- Abstract
- In this paper, we propose a novel inverse reinforcement learning algorithm with leveraged Gaussian processes that can learn from both positive and negative demonstrations. While most existing inverse reinforcement learning (IRL) methods suffer from the lack of information near low reward regions, the proposed method alleviates this issue by incorporating (negative) demonstrations of what not to do. To mathematically formulate negative demonstrations, we introduce a novel generative model which can generate both positive and negative demonstrations using a parameter, called proficiency. Moreover, since we represent a reward function using a leveraged Gaussian process which can model a nonlinear function, the proposed method can effectively estimate the structure of a nonlinear reward function.
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Collections - College of Software > Department of Artificial Intelligence > 1. Journal Articles
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