34 citations · 174 across the 26 of their papers we have counts for
4 papers · 1 filter
Formally-Sharp DAgger for MCTS: Lower-Latency Monte Carlo Tree Search using Data Aggregation with Formal Methods
Debraj Chakraborty, Damien Busatto-Gaston, Jean-François Raskin +1
We study how to efficiently combine formal methods, Monte Carlo Tree Search (MCTS), and deep learning in order to produce high-quality receding horizon policies in large Markov Dec…
Lecture Notes on Partially Known MDPs
Guillermo A. Perez
In these notes we will tackle the problem of finding optimal policies for Markov decision processes (MDPs) which are not fully known to us. Our intention is to slowly transition fr…
Learning-Based Mean-Payoff Optimization in an Unknown MDP under Omega-Regular Constraints
Jan Křetínský, Guillermo A. Pérez, Jean-François Raskin
We formalize the problem of maximizing the mean-payoff value with high probability while satisfying a parity objective in a Markov decision process (MDP) with unknown probabilistic…
Optimizing Expectation with Guarantees in POMDPs (Technical Report)
Krishnendu Chatterjee, Petr Novotný, Guillermo A. Pérez +2
A standard objective in partially-observable Markov decision processes (POMDPs) is to find a policy that maximizes the expected discounted-sum payoff. However, such policies may st…