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Deep Reinforcement and InfoMax Learning
Bogdan Mazoure · Remi Tachet des Combes · Thang Long Doan · Philip Bachman · R Devon Hjelm

Tue Dec 08 09:00 AM -- 11:00 AM (PST) @ Poster Session 1 #548

We posit that a reinforcement learning (RL) agent will perform better when it uses representations that are better at predicting the future, particularly in terms of few-shot learning and domain adaptation. To test that hypothesis, we introduce an objective based on Deep InfoMax (DIM) which trains the agent to predict the future by maximizing the mutual information between its internal representation of successive timesteps. We provide an intuitive analysis of the convergence properties of our approach from the perspective of Markov chain mixing times, and argue that convergence of the lower bound on mutual information is related to the inverse absolute spectral gap of the transition model. We test our approach in several synthetic settings, where it successfully learns representations that are predictive of the future. Finally, we augment C51, a strong distributional RL agent, with our temporal DIM objective and demonstrate on a continual learning task (inspired by Ms.~PacMan) and on the recently introduced Procgen environment that our approach improves performance, which supports our core hypothesis.

Author Information

Bogdan Mazoure (McGill University)

Ph.D. student at MILA / McGill University, supervised by Doina Precup and Devon Hjelm. Interested in reinforcement learning, representation learning, mathematical statistics and density estimation.

Remi Tachet des Combes (Microsoft Research Montreal)
Thang Long Doan (McGill)
Philip Bachman (Microsoft Research)
R Devon Hjelm (Microsoft Research)

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