Cybernetics: or the Control and Communication in the Animal by Norbert Wiener

By Norbert Wiener

Acclaimed one of many "seminal books... related in final value to... Galileo or Malthus or Rousseau or Mill", Cybernetics was once judged by way of twenty-seven historians, economists, educators, and philosophers to be a type of books released in the course of the "past 4 decades," that could have a considerable effect on public idea and motion within the years ahead." -- Saturday Review

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Extra resources for Cybernetics: or the Control and Communication in the Animal and the Machine (2nd Edition)

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From a simple reward structure the agent should be able to figure out the policy its designer had in mind. Simple rewards soon prove to be inefficient. Take as an example a simple ’go to ball’ behaviour. The simplest reward structure is a reward for getting to the ball and a penalty for every time step. This reward structure soon proved to be inefficient as an effect of a reward at the end of the entire training episode is propagated back in time only for a certain amount of steps according to the eligibility trace, meaning that the agent cannot ’feel’ the goal when he is more than the eligibility trace length (in time) away from it.

To examine the effect of surface Gaussian curvature K on the geodesic curvature κg of the offset curves, we apply the Gauss-Bonnet theorem to the region B bounded between the start curve, Cst , and its offset curve, Cof , (see Fig. 3(a)) and arrive at    κg . (1) K+ κg = Cof B Cst Equation 1 tells us that the more the surface bounded between the offset curve and the geodesic start curve bends, the more the geodesic curvature of the offset curve increases. On a surface where the sign of the Gaussian curvature stays the same, the further away we place the offset curve from the start curve, the higher the geodesic curvature of the offset curve is.

In high dimensional spaces the agent is very likely to land in local minima. Different policies tend to interfere with each other. With Q-learning in particular, extra inputs which are un- or weakly correlated to the rewards and therefore irrelevant, make the system perform less. This is inherent to the algorithm, as only the positive part of the ’noise’ is propagated forward, biasing the system. High dimensionality along with the inability to reject irrelevant data and poor generalization, results in poor performances.

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