Value iteration is a powerful yet inefficient algorithm for Markov decision processes (MDPs)
because it puts the majority of its effort into backing up the entire state space, which turns out …

This paper describes GPU based algorithms to compute state transition models for
unmanned surface vehicles (USVs) using 6 degree of freedom (DOF) dynamics simulations …

Markov decision processes (MDPs) provide a rich framework for planning under uncertainty.
However, exactly solving a large MDP is usually intractable due to the “curse of …

Abstract Markov Decision Processes have become the standard model for probabilistic
planning. However, when applied to many practical problems, the estimates of transition …

This paper investigates optimal power flow management problem in an electric vehicle
charging station. The charging station is powered by solar PV and is tied to the grid and a …

This work presents the concept of Continuous Search (CS), which objective is to allow any
user to eventually get their constraint solver achieving a top performance on their problems …

We present a look-ahead based trajectory planning algorithm for computation of dynamically
feasible trajectories for Unmanned Sea Surface Vehicles (USSV) operating in high seas …

We introduce the lookahead-bounded Q-learning (LBQL) algorithm, a new, provably
convergent variant of Q-learning that seeks to improve the performance of standard Q …

Current methods for solving Stochastic Shortest Path Problems (SSPs) find states' costs-to-
go by applying Bellman backups, where state-of-the-art methods employ heuristics to select …

We consider the problem of estimation of an unknown real valued function with real valued
input by an agent. The agent exists in 3D Euclidean space. It is able to traverse in a 2D …