Information-Theoretic World Model learning for Denoised Predictions

Vedant Dave, Elmar Rueckert

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Keywords: Representation Learning, Predictive Information, Information Bottleneck, SAC

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Abstract: Humans excel at isolating relevant information from noisy data to predict the behavior of dynamic systems, effectively disregarding non-informative, temporally-correlated noise. In contrast, existing reinforcement learning algorithms face challenges in generating noise-free predictions within high-dimensional, noise-saturated environments, especially when trained on world models featuring realistic background noise extracted from natural video streams. We propose a novel information-theoretic approach that learns world models based on minimising the past information and retaining maximal information about the future, aiming at simultaneously learning control policies and at producing denoised predictions. Utilizing Soft Actor-Critic agents augmented with an information-theoretic auxiliary loss, we validate our method's effectiveness on complex variants of the standard DeepMind Control Suite tasks, where natural videos filled with intricate and task-irrelevant information serve as a background. Experimental results indicate that our model surpasses four state-of-the-art approaches across six distinct scenarios where natural videos serve as dynamic background noise, all while maintaining competitive performance in traditional settings

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Submission Number: 7554