An Optimality Principle Governing Human Walking

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An Optimality Principle for Concurrent Systems

This paper presents a formulation of an optimality principle for a new class of concurrent decision systems formed by products of deterministic Markov decision processes (MDPs). For a single MDP, the optimality principle reduces to the usual Bellman’s equation. The formulation is significant because it provides a basis for the development of optimisation algorithms for decentralised decision sy...

An Optimality Principle for Unsupervised Learning

We propose an optimality principle for training an unsupervised feedforward neural network based upon maximal ability to reconstruct the input data from the network outputs. We describe an algorithm which can be used to train either linear or nonlinear networks with certain types of nonlinearity. Examples of applications to the problems of image coding, feature detection, and analysis of random...

An Information-Theoretic Optimality Principle for Deep Reinforcement Learning

We methodologically address the problem of Qvalue overestimation in deep reinforcement learning to handle high-dimensional state spaces efficiently. By adapting concepts from information theory, we introduce an intrinsic penalty signal encouraging reduced Q-value estimates. The resultant algorithm encompasses a wide range of learning outcomes containing deep Q-networks as a special case. Differ...

A Principle Governing Growth Orientation in Vapour-deposited Layers

The preferred orientation ofvapour-deposited layers can originate from several stages of the evaporation. It is shown that an increase in the orientation sharpness during the vertical growth points to a process of "evolutionary selection". This process is discussed and visualized. The orientation is calculated as a function of the angle of incidence for a number of typical, most extreme, cases....