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We demonstrate equivalence between the reinforcement learning problem and thesupervised classification problem. We consequently equate the explorationexploitation trade-off in reinforcement learning to the dataset imbalanceproblem in supervised classification, and find similarities in how they areaddressed. From our analysis of the aforementioned problems we derive a novelloss function for reinforcement learning and supervised classification. ScopeLoss, our new loss function, adjusts gradients to prevent performance lossesfrom over-exploitation and dataset imbalances, without the need for any tuning.We test Scope Loss against SOTA loss functions over a basket of benchmarkreinforcement learning tasks and a skewed classification dataset, and show thatScope Loss outperforms other loss functions.

Scope Loss for Imbalanced Classification and RL Exploration