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Poster
Planning in entropy-regularized Markov decision processes and games
Jean-Bastien Grill · Omar Darwiche Domingues · Pierre Menard · Remi Munos · Michal Valko

Thu Dec 12 10:45 AM -- 12:45 PM (PST) @ East Exhibition Hall B + C #217
in Reinforcement Learning and Planning -- Planning »
We propose SmoothCruiser, a new planning algorithm for estimating the value function in entropy-regularized Markov decision processes and two-player games, given a generative model of the SmoothCruiser. SmoothCruiser makes use of the smoothness of the Bellman operator promoted by the regularization to achieve problem-independent sample complexity of order $\tilde{\mathcal{O}}(1/\epsilon^4)$ for a desired accuracy $\epsilon$, whereas for non-regularized settings there are no known algorithms with guaranteed polynomial sample complexity in the worst case.
Keywords: Reinforcement Learning and Planning · Reinforcement Learning and Planning -> Markov Decision Processes; Reinforcement Learning and Planning -> Planning; Reinforcemen
[ Paper]  [ Poster] 

Author Information

Jean-Bastien Grill (Google DeepMind)
Omar Darwiche Domingues (Inria)
Pierre Menard (Inria)
Remi Munos (DeepMind)
Michal Valko (DeepMind Paris and Inria Lille - Nord Europe)

Michal is a research scientist in DeepMind Paris and SequeL team at Inria Lille - Nord Europe, France, lead by Philippe Preux and Rémi Munos. He also teaches the course Graphs in Machine Learning at l'ENS Cachan. 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) minimising the data that humans need spend inspecting, classifying, or “tuning” the algorithms. Another important feature of machine learning algorithms should be the ability to adapt to changing environments. That is why he is working in domains that are able to deal with minimal feedback, such as semi-supervised learning, bandit algorithms, and anomaly detection. The common thread of Michal's work has been adaptive graph-based learning and its application to the real world applications such as recommender systems, medical error detection, and face recognition. His industrial collaborators include Intel, Technicolor, and Microsoft Research. He received his PhD in 2011 from University of Pittsburgh under the supervision of Miloš Hauskrecht and after was a postdoc of Rémi Munos.

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