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Conference Papers Year : 2012

APRIL: Active Preference-learning based Reinforcement Learning


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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hal-00722744 , version 1 (03-08-2012)



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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