6 papers
Noise-aware Verification and Synthesis of Quantum Programs
Stefanie Muroya, Krishnendu Chatterjee, Thomas A. Henzinger
While most research on quantum programming considers an idealized, noise-free semantics for quantum programs, we reason about quantum programs that are executed on real, noisy hard…
Reinforcement Learning for Reachability: Guaranteeing Asymptotic Optimality
Amogh Palasamudram, Jakub Svoboda, Suguman Bansal +1
Reinforcement learning (RL) for reachability specifications is fundamental in sequential decision-making, yet theoretical guarantees remain less explored. A recent work achieves as…
Multi-Environment POMDPs with Finite-Horizon Objectives
Léonard Brice, Filip Cano, Krishnendu Chatterjee +2
Partially Observable Markov Decision Processes (POMDPs) are systems in which one agent interacts with a stochastic environment, and receives only partial information about the curr…
On the Complexity of Discounted Robust MDPs with Uncertainty Sets
Ali Asadi, Krishnendu Chatterjee, Alipasha Montaseri +1
A basic model in sequential decision making is the Markov decision process (MDP), which is extended to Robust MDPs (RMDPs) by allowing uncertainty in transition probabilities and o…
Multiplicative Rewards in Markovian Models
Christel Baier, Krishnendu Chatterjee, Tobias Meggendorfer +1
This paper studies the expected value of multiplicative rewards, where rewards obtained in each step are multiplied (instead of the usual addition), in Markov chains (MCs) and Mark…
The Value Problem for Multiple-Environment MDPs with Parity Objective
Krishnendu Chatterjee, Laurent Doyen, Jean-François Raskin +1
We consider multiple-environment Markov decision processes (MEMDP), which consist of a finite set of MDPs over the same state space, representing different scenarios of transition…