— This paper addresses model reduction for a Markov chain on a large state space. A simulation-based framework is introduced to perform state aggregation of the Markov chain base...
Abstract— Q-learning is a technique used to compute an optimal policy for a controlled Markov chain based on observations of the system controlled using a non-optimal policy. It ...
Comprehensive exploration of the design space parameters at the system-level is a crucial task to evaluate architectural tradeoffs accounting for both energy and performance const...
William Fornaciari, Donatella Sciuto, Cristina Sil...
—In the context of multiple constant multiplications (MCM) design, we propose a novel common-subexpression-elimination (CSE) algorithm that models synthesis of coefficients into ...
Trajectory planning and optimization is a fundamental problem in articulated robotics. Algorithms used typically for this problem compute optimal trajectories from scratch in a ne...