Abstract. We present experimental results about learning function values (i.e. Bellman values) in stochastic dynamic programming (SDP). All results come from openDP (opendp.sourcef...
We present a novel dynamic analysis technique that finds real deadlocks in multi-threaded programs. Our technique runs in two stages. In the first stage, we use an imprecise dyn...
Pallavi Joshi, Chang-Seo Park, Koushik Sen, Mayur ...
Partially-observable Markov decision processes (POMDPs) provide a powerful model for sequential decision-making problems with partially-observed state and are known to have (appro...
In this paper, we consider the problem of planning and learning in the infinite-horizon discounted-reward Markov decision problems. We propose a novel iterative direct policysearc...
Abstract. We provide a framework for the design and analysis of dynamic programming algorithms for surface-embedded graphs on n vertices and branchwidth at most k. Our technique ap...