30 citations · 34 across the 9 of their papers we have counts for
16 papers
Momba: Network Modernization Improves Multi-Objective Reinforcement Learning
Adam Štafa, Santeri Heiskanen, Petr Novotný +1
Recent advances in deep reinforcement learning (RL) have shown that improving neural network architectures can yield substantial gains in sample efficiency and asymptotic performan…
Refuting Equivalence in Probabilistic Programs with Conditioning
Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný +1
We consider the problem of refuting equivalence of probabilistic programs, i.e., the problem of proving that two probabilistic programs induce different output distributions. We st…
Equivalence and Similarity Refutation for Probabilistic Programs
Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný +1
We consider the problems of statically refuting equivalence and similarity of output distributions defined by a pair of probabilistic programs. Equivalence and similarity are two f…
Solving Long-run Average Reward Robust MDPs via Stochastic Games
Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Mehrdad Karrabi +2
Markov decision processes (MDPs) provide a standard framework for sequential decision making under uncertainty. However, MDPs do not take uncertainty in transition probabilities in…
Proving Non-termination by Program Reversal
Krishnendu Chatterjee, Ehsan Kafshdar Goharshady, Petr Novotný +1
We present a new approach to proving non-termination of non-deterministic integer programs. Our technique is rather simple but efficient. It relies on a purely syntactic reversal o…
Reinforcement Learning of Risk-Constrained Policies in Markov Decision Processes
Tomas Brazdil, Krishnendu Chatterjee, Petr Novotny +1
Markov decision processes (MDPs) are the defacto frame-work for sequential decision making in the presence ofstochastic uncertainty. A classical optimization criterion forMDPs is t…