R-max is a very simple model-based reinforcement learning algorithm which can attain near-optimal average reward in polynomial time. In R-max, the agent always maintains a complet...
Abstract— The role of gradient estimation in global optimization is investigated. The concept of a regional gradient is introduced as a tool for analyzing and comparing different...
Monotonicity is a simple yet significant qualitative characteristic. We consider the problem of segmenting an array in up to K segments. We want segments to be as monotonic as po...
A new, self-stabilizing algorithm for electing a leader on a unidirectional ring of prime size is presented for the composite atomicity model with a centralized daemon. Its space c...
In this paper, we propose a novel deadlock resolution algorithm. In the presence of global deadlocks in a multidatabase system, this algorithm always selects an optimal set of vic...