Potential based reward shaping using learning to rank

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Conference Proceeding
ACM/IEEE International Conference on Human-Robot Interaction, 2017, pp. 261 - 262
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© 2017 Authors. This paper presents a novel method for the computation of potential function using human input for potential based reward shaping. It defines a ranking over state space which is used to define a potential function. Specifically, it seeks multiple, partial to full, rankings of robot's states from a user in a HRI scenario. These rankings are used to learn a ranking model using a learning-to-rank algorithm. The ranking model is used to define a complete ranking of states. From the ranked states, a potential function is computed using a mapping function. For the proof of concept, we compared it with a base-line reinforcement learner in a simulated domain. The empirical results showed that the proposed method clearly outperformed the benchmark.
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