Safe Imitation Learning via Fast Bayesian Reward Inference from Preferences

Jul 12, 2020

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Bayesian reward learning from demonstrations enables rigorous safety and uncertainty analysis when performing imitation learning. However, Bayesian reward learning methods are typically computationally intractable for complex control problems. We propose a highly efficient Bayesian reward learning algorithm that scales to high-dimensional imitation learning problems by first pre-training a low-dimensional feature encoding via self-supervised tasks and then leveraging preferences over demonstrations to perform fast Bayesian inference via sampling. We evaluate our proposed approach on the task of learning to play Atari games from demonstrations, without access to the game score, and achieve state-of-the-art imitation learning performance. Furthermore, we also demonstrate that our approach enables efficient high-confidence performance bounds for any evaluation policy. We show that these high-confidence performance bounds can be used to accurately rank the performance and risk of a variety of different evaluation policies, despite not having samples of the true reward function.

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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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