Issues in Automated Distribution of Processes Over the Networks

The main goal of this paper is t o survey the issues an application developer would have to resolve in producing a system that would be able to spread its computational load across several computers connected by a network. Before this can be done, a brief introduction to distributed and parallel computing is necessary

A Computational Study of the Performance and Robustness Properties of Retrospective Cost Adaptive Control

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/83646/1/AIAA-2010-8011-332.pd

Generative Flow Networks as Entropy-Regularized RL

The recently proposed generative flow networks (GFlowNets) are a method of training a policy to sample compositional discrete objects with probabilities proportional to a given reward via a sequence of actions. GFlowNets exploit the sequential nature of the problem, drawing parallels with reinforcement learning (RL). Our work extends the connection between RL and GFlowNets to a general case. We demonstrate how the task of learning a generative flow network can be efficiently redefined as an entropy-regularized RL problem with a specific reward and regularizer structure. Furthermore, we illustrate the practical efficiency of this reformulation by applying standard soft RL algorithms to GFlowNet training across several probabilistic modeling tasks. Contrary to previously reported results, we show that entropic RL approaches can be competitive against established GFlowNet training methods. This perspective opens a direct path for integrating reinforcement learning principles into the realm of generative flow networks

On the Accuracy of State Estimators for Constant and Time-Varying Parameter Estimation

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/106504/1/AIAA2013-5192.pd