Interactive Value Iteration for Markov Decision Processes with Unknown Rewards

Paul Weng (1) , Bruno Zanuttini (2)

1 DECISION
2 Equipe MAD - Laboratoire GREYC - UMR6072

To tackle the potentially hard task of defining the reward function in a Markov Decision Process, we propose a new approach, based on Value Iteration, which interweaves the elicitation and optimization phases. We assume that rewards whose numeric values are unknown can only be ordered, and that a tutor is present to help comparing sequences of re- wards. We first show how the set of possible reward functions for a given preference relation can be rep- resented as a polytope. Then our algorithm, called Interactive Value Iteration, searches for an optimal policy while refining its knowledge about the pos- sible reward functions, by querying a tutor when necessary. We prove that the number of queries needed before finding an optimal policy is upper- bounded by a polynomial in the size of the problem, and we present experimental results which demon- strate that our approach is efficient in practice.

Domaines: Système multi-agents [cs.MA]; Intelligence artificielle [cs.AI]; Modélisation et simulation; Apprentissage [cs.LG]

Paul Weng, Bruno Zanuttini. Interactive Value Iteration for Markov Decision Processes with Unknown Rewards. IJCAI '13 - Twenty-Third international joint conference on Artificial Intelligence, Aug 2013, Beijing, China. pp.2415-2421. ⟨hal-00942290⟩