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APRIL: Active Preference-learning based Reinforcement Learning

Riad Akrour 1, 2 Marc Schoenauer 1, 2 Michèle Sebag 2
1 TAO - Machine Learning and Optimisation
CNRS - Centre National de la Recherche Scientifique : UMR8623, Inria Saclay - Ile de France, UP11 - Université Paris-Sud - Paris 11, LRI - Laboratoire de Recherche en Informatique
Abstract : This paper focuses on reinforcement learning (RL) with limited prior knowledge. In the domain of swarm robotics for instance, the expert can hardly design a reward function or demonstrate the target behavior, forbidding the use of both standard RL and inverse reinforcement learning. Although with a limited expertise, the human expert is still often able to emit preferences and rank the agent demonstrations. Earlier work has presented an iterative preference-based RL framework: expert preferences are exploited to learn an approximate policy return, thus enabling the agent to achieve direct policy search. Iteratively, the agent selects a new candidate policy and demonstrates it; the expert ranks the new demonstration comparatively to the previous best one; the expert's ranking feedback enables the agent to refine the approximate policy return, and the process is iterated. In this paper, preference-based reinforcement learning is combined with active ranking in order to decrease the number of ranking queries to the expert needed to yield a satisfactory policy. Experiments on the mountain car and the cancer treatment testbeds witness that a couple of dozen rankings enable to learn a competent policy.
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Submitted on : Friday, August 3, 2012 - 5:50:20 PM
Last modification on : Thursday, July 8, 2021 - 3:48:24 AM
Long-term archiving on: : Friday, December 16, 2016 - 5:15:39 AM


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  • HAL Id : hal-00722744, version 1
  • ARXIV : 1208.0984



Riad Akrour, Marc Schoenauer, Michèle Sebag. APRIL: Active Preference-learning based Reinforcement Learning. ECML PKDD 2012, Sep 2012, Bristol, United Kingdom. pp.116-131. ⟨hal-00722744⟩



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