852 research outputs found
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
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