- Keywords: Deep Feedforward Neural Network, Game Theory, Control Theory, Reinforcement Learning, Cellular Automata
- TL;DR: Reversed Neural Network - A Primal
- Abstract: Contrary to most reinforcement learning research, which emphasizes on training a deep neural network to have its output layer to approximate a certain strategy, this paper proposes a revolutionary and a reversed method of reinforcement learning. We call this “Reversed Neural Network”. In short, after we train a deep neural network according to a strategy-and-environment-to-payoff table well enough, then we use back-propagation algorithm and propagate the error between the actual output and the desired output back to the “input layer” of a deep neural network gradually to perform a task similar to “human deduction”. And we view the final “input layer” as the fittest strategy for a neural network in a natural environment.