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This paper introduces a method for learning structured latent representations in RL where distances reflect transition costs, providing a geometric interpretation of uncertainty without explicit probabilistic modeling. They achieve this with a multimodal latent transition model and inverse distance weighting for sensor fusion, enabling adaptive integration of multiple sensor modalities. Empirical validation on multimodal RL tasks demonstrates improved robustness to sensor noise, superior state estimation, and enhanced RL agent performance compared to baselines, eliminating the need for noise augmentation.
Forget probabilistic noise models: this new approach learns a latent space where state distances directly encode transition costs, enabling robust multimodal RL.
Estimating the state of an environment from high-dimensional, multimodal, and noisy observations is a fundamental challenge in reinforcement learning (RL). Traditional approaches rely on probabilistic models to account for the uncertainty, but often require explicit noise assumptions, in turn limiting generalization. In this work, we contribute a novel method to learn a structured latent representation, in which distances between states directly correlate with the minimum number of actions required to transition between them. The proposed metric space formulation provides a geometric interpretation of uncertainty without the need for explicit probabilistic modeling. To achieve this, we introduce a multimodal latent transition model and a sensor fusion mechanism based on inverse distance weighting, allowing for the adaptive integration of multiple sensor modalities without prior knowledge of noise distributions. We empirically validate the approach on a range of multimodal RL tasks, demonstrating improved robustness to sensor noise and superior state estimation compared to baseline methods. Our experiments show enhanced performance of an RL agent via the learned representation, eliminating the need of explicit noise augmentation. The presented results suggest that leveraging transition-aware metric spaces provides a principled and scalable solution for robust state estimation in sequential decision-making.
