We describe an approach for exploiting structure in Markov Decision Processes with continuous state variables. At each step of the dynamic programming, the state space is dynamica...
Zhengzhu Feng, Richard Dearden, Nicolas Meuleau, R...
Abstract--Higher circuit densities in system-on-chip (SOC) designs have led to drastic increase in test data volume. Larger test data size demands not only higher memory requiremen...
The crew planning problem has been successfully solved on a loosely connected network of workstations (NOW) using advanced computational techniques and efficient communication pat...
Abstract. We present an implementation of model-based online reinforcement learning (RL) for continuous domains with deterministic transitions that is specifically designed to achi...
Partially observable Markov decision processes (POMDPs) are widely used for planning under uncertainty. In many applications, the huge size of the POMDP state space makes straightf...
Joni Pajarinen, Jaakko Peltonen, Ari Hottinen, Mik...