Accountable Off-Policy Evaluation via a Kernelized Bellman Statistics

Jul 12, 2020

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Off-policy evaluation plays an important role in modern reinforcement learning. However, most of the existing off-policy evaluation only focus on the value estimation, without providing an accountable confidence interval, that can reflect the uncertainty caused by limited observed data and algorithmic errors. Recently, Feng et al. (2019) proposed a novel kernel loss for learning value functions, which can also be used to test whether the learned value function satisfies the Bellman equation. In this work, we investigate the statistical properties of the kernel loss, which allows us to find a feasible set that contains the true value function with high probability. We further utilize this set to construct an accountable confidence interval for off-policy value estimation, and a post-hoc diagnosis for existing estimators. Empirical results show that our methods yield a tight yet accountable confidence interval in different settings, which demonstrate the effectiveness of our method.

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About ICML 2020

The International Conference on Machine Learning (ICML) is the premier gathering of professionals dedicated to the advancement of the branch of artificial intelligence known as machine learning. ICML is globally renowned for presenting and publishing cutting-edge research on all aspects of machine learning used in closely related areas like artificial intelligence, statistics and data science, as well as important application areas such as machine vision, computational biology, speech recognition, and robotics. ICML is one of the fastest growing artificial intelligence conferences in the world. Participants at ICML span a wide range of backgrounds, from academic and industrial researchers, to entrepreneurs and engineers, to graduate students and postdocs.

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