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In this talk we offer a generative perspective on value function approximation in reinforcement learning. Based on this perspective we develop the Compress and Control algorithm, which transforms arbitrary density estimators into value functions. In particular, we consider compression methods such as the Lempel-Ziv and Context Tree Switching algorithms as base models. The appeal of compression methods for density estimation is that they are in a sense feature-free: they can be tractably applied to bit sequences, and therefore to any kind of data. Along with a theoretical overview of the method, we present empirical results on the Atari 2600 platform.
Reference: webdocs.cs.ualberta.ca/~mg17/publications/veness14compress.pdf
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Marc G. Bellemare received his Ph.D. from the University of Alberta, where he investigated the concept of domain-independent agents and led the design of the Arcade Learning Environment. His research interests include reinforcement learning, online learning, information theory, lifelong learning, and randomized algorithms. He is currently at Google DeepMind.
Joel Veness is a Senior Research Scientist at Google DeepMind. He is interested in reinforcement learning, universal source coding, Bayesian nonparametrics and game AI.
