Observational Overfitting in Reinforcement Learning
international conference on learning representations, 2020.
We have identified and isolated a key component of overfitting in reinforcement learning as the particular case of “observational overfitting”, which is attractive for studying architectural implicit regularizations
A major component of overfitting in model-free reinforcement learning (RL) involves the case where the agent may mistakenly correlate reward with certain spurious features from the observations generated by the Markov Decision Process (MDP). We provide a general framework for analyzing this scenario, which we use to design multiple synthe...More
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