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Interpretability in Reinforcement Learning Agents Regularization and visualization of attention in reinforcement learning agents Dmitry Nikulin , Sebastian Kosch , Fabian Steuer , Hoagy Cunningham Github Repository This project was completed during AI Safety Camp 3 in Ávila, Spain, in May 2019. Introduction Advances in deep learning are enabling reinforcement learning (RL) agents to accomplish increasingly difficult tasks. For instance, relatively simple machine learning agents can learn how to beat humans in video games, without ever having been programmed how to do so. However, agents sometimes learn to make correct decisions for the wrong reasons, which can lead to surprising and perplexing failures later. In order to diagnose such problems effectively, the developer needs to understand how information flows through the artificial neural network that powers the agent's d...
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