Jun 14, 2019
Conventionally, reinforcement learning is considered to be a framework for optimization: the aim for standard reinforcement learning algorithms is to recover an optimal or near-optimal policy that maximizes the reward over time. However, when considering more advanced reinforcement learning problems, from inverse reinforcement learning to unsupervised and hierarchical reinforcement learning, we often encounter settings where it is desirable to learn policies that match target distributions over trajectories or states, covering all modes, or else to simply learn collections of behaviors that are as broad and varied as possible. Information theory and probabilistic inference offer is a powerful set of tools for developing algorithms for these kinds of distribution matching problems. In this talk, I will outline methods that combine reinforcement learning, inference, and information theory to learn policies that match target distributions and acquire diverse behaviors, and discuss the applications of such methods for a variety of problems in artificial intelligence and robotics.
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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