Artificial intelligence has begun to play a critical role in basic science research. In high energy physics, AI methods can aid precision measurements that elucidate the underlyi...
In this article, we propose a method to adapt stepsize parameters used in reinforcement learning for dynamic environments. In general reinforcement learning situations, a stepsize...
We present a technique for computing approximately optimal solutions to stochastic resource allocation problems modeled as Markov decision processes (MDPs). We exploit two key pro...
Nicolas Meuleau, Milos Hauskrecht, Kee-Eung Kim, L...
This paper presents MPIAB, an agent based architecture for parallel processing. The architecture is developed to model the functions of standard MPI using java agents. It remedies...
We present an efficient "sparse sampling" technique for approximating Bayes optimal decision making in reinforcement learning, addressing the well known exploration vers...
Tao Wang, Daniel J. Lizotte, Michael H. Bowling, D...