Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...
Efficiently utilizing off-chip DRAM bandwidth is a critical issue in designing cost-effective, high-performance chip multiprocessors (CMPs). Conventional memory controllers deli...
In this paper, we propose and develop a novel approach to the problem of optimally managing the tax, and more generally debt, collections processes at financial institutions. Our...
Naoki Abe, Prem Melville, Cezar Pendus, Chandan K....
In this paper, we present a novel multi-agent learning paradigm called team-partitioned, opaque-transition reinforcement learning (TPOT-RL). TPOT-RL introduces the concept of usin...
A number of today's state-of-the-art planners are based on forward state-space search. The impressive performance can be attributed to progress in computing domain independen...