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arXiv · 1802.03006

Learning and Querying Fast Generative Models for Reinforcement Learning

Lars Buesing·Theophane Weber·Sebastien Racaniere·S. M. Ali Eslami·Danilo Rezende·David P. Reichert·Fabio Viola·Frederic Besse·Karol Gregor·Demis Hassabis·Daan Wierstra
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Abstract

A key challenge in model-based reinforcement learning (RL) is to synthesize computationally efficient and accurate environment models. We show that carefully designed generative models that learn and operate on compact state representations, so-called state-space models, substantially reduce the computational costs for predicting outcomes of sequences of actions. Extensive experiments establish that state-space models accurately capture the dynamics of Atari games from the Arcade Learning Environment from raw pixels. The computational speed-up of state-space models while maintaining high accuracy makes their application in RL feasible: We demonstrate that agents which query these models for decision making outperform strong model-free baselines on the game MSPACMAN, demonstrating the potential of using learned environment models for planning.

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Lars Buesing, Theophane Weber, Sebastien Racaniere, S. M. Ali Eslami, Danilo Rezende, David P. Reichert, Fabio Viola, Frederic Besse, Karol Gregor, Demis Hassabis, Daan Wierstra. 2018-02-08. Learning and Querying Fast Generative Models for Reinforcement Learning. https://arxiv.org/abs/1802.03006

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