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Poster
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity
Anders Jonsson · Emilie Kaufmann · Pierre Menard · Omar Darwiche Domingues · Edouard Leurent · Michal Valko

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #360
in Poster Session 2 »

We propose MDP-GapE, a new trajectory-based Monte-Carlo Tree Search algorithm for planning in a Markov Decision Process in which transitions have a finite support. We prove an upper bound on the number of sampled trajectories needed for MDP-GapE to identify a near-optimal action with high probability. This problem-dependent result is expressed in terms of the sub-optimality gaps of the state-action pairs that are visited during exploration. Our experiments reveal that MDP-GapE is also effective in practice, in contrast with other algorithms with sample complexity guarantees in the fixed-confidence setting, that are mostly theoretical.

Author Information

Anders Jonsson (Universitat Pompeu Fabra)
Emilie Kaufmann (CNRS)
Pierre Menard (Inria)
Omar Darwiche Domingues (Inria)
Edouard Leurent (INRIA)

PhD student in Reinforcement Learning, at: - INRIA SequeL project for sequential learning - INRIA Non-A project for finite-time control - Renault Group

Michal Valko (DeepMind)
Michal Valko

Michal is a machine learning scientist in DeepMind Paris, tenured researcher at Inria, and the lecturer of the master course Graphs in Machine Learning at l'ENS Paris-Saclay. Michal is primarily interested in designing algorithms that would require as little human supervision as possible. This means 1) reducing the “intelligence” that humans need to input into the system and 2) minimizing the data that humans need to spend inspecting, classifying, or “tuning” the algorithms. That is why he is working on methods and settings that are able to deal with minimal feedback, such as deep reinforcement learning, bandit algorithms, or self-supervised learning. Michal is actively working on represenation learning and building worlds models. He is also working on deep (reinforcement) learning algorithm that have some theoretical underpinning. He has also worked on sequential algorithms with structured decisions where exploiting the structure leads to provably faster learning. He received his Ph.D. in 2011 from the University of Pittsburgh under the supervision of Miloš Hauskrecht and after was a postdoc of Rémi Munos before taking a permanent position at Inria in 2012.

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