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Two-level Q-learning: learning from conflict demonstrations

Part of: Adaptive Learning Agents 2018

Published online by Cambridge University Press:  12 November 2019

Mao Li Open the ORCID record for Mao Li [Opens in a new window] ,
Yi Wei  and
Daniel Kudenko
Mao Li*
Affiliation:
Computer Science Department, University of York, York, United Kingdom e-mail: [email protected]
Yi Wei
Affiliation:
Computer Science Department, Shan dong University, Jinan, China e-mail: [email protected]
Daniel Kudenko
Affiliation:
Computer Science Department, University of York, York, United Kingdom e-mail: [email protected] JetBrains Research, St Petersburg, Russia e-mail: [email protected]
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Abstract

One way to address this low sample efficiency of reinforcement learning (RL) is to employ human expert demonstrations to speed up the RL process (RL from demonstration or RLfD). The research so far has focused on demonstrations from a single expert. However, little attention has been given to the case where demonstrations are collected from multiple experts, whose expertise may vary on different aspects of the task. In such scenarios, it is likely that the demonstrations will contain conflicting advice in many parts of the state space. We propose a two-level Q-learning algorithm, in which the RL agent not only learns the policy of deciding on the optimal action but also learns to select the most trustworthy expert according to the current state. Thus, our approach removes the traditional assumption that demonstrations come from one single source and are mostly conflict-free. We evaluate our technique on three different domains and the results show that the state-of-the-art RLfD baseline fails to converge or performs similarly to conventional Q-learning. In contrast, the performance level of our novel algorithm increases with more experts being involved in the learning process and the proposed approach has the capability to handle demonstration conflicts well.

Type
Adaptive and Learning Agents
Information
The Knowledge Engineering Review , Volume 34 , 2019 , e14
Copyright
© Cambridge University Press 2019 

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