
Tournament selection in zerothlevel classifier systems based on average reward reinforcement learning
As a geneticsbased machine learning technique, zerothlevel classifier ...
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Reinforcement Learning with Trajectory Feedback
The computational model of reinforcement learning is based upon the abil...
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Is Epicurus the father of Reinforcement Learning?
The Epicurean Philosophy is commonly thought as simplistic and hedonisti...
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Evolutionary reinforcement learning of dynamical large deviations
We show how to calculate dynamical large deviations using evolutionary r...
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Sparsely ensembled convolutional neural network classifiers via reinforcement learning
We consider convolutional neural network (CNN) ensemble learning with th...
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Enhancing reinforcement learning by a finite reward response filter with a case study in intelligent structural control
In many reinforcement learning (RL) problems, it takes some time until a...
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On the Mathematical Understanding of ResNet with Feynman Path Integral
In this paper, we aim to understand Residual Network (ResNet) in a scien...
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Learning Principle of Least Action with Reinforcement Learning
Nature provides a way to understand physics with reinforcement learning since nature favors the economical way for an object to propagate. In the case of classical mechanics, nature favors the object to move along the path according to the integral of the Lagrangian, called the action 𝒮. We consider setting the reward/penalty as a function of 𝒮, so the agent could learn the physical trajectory of particles in various kinds of environments with reinforcement learning. In this work, we verified the idea by using a QLearning based algorithm on learning how light propagates in materials with different refraction indices, and show that the agent could recover the minimaltime path equivalent to the solution obtained by Snell's law or Fermat's Principle. We also discuss the similarity of our reinforcement learning approach to the path integral formalism.
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