Latent Action Discretization in DiLA for Robust Multimodal Language-Conditioned Policies Under Visual Perception Noise on CALVIN

Abstract

Abstract: This paper investigates the resilience and robustness of Deep Reinforcement Learning (DRL) policies to adversarial perturbations in the state space. We first present an approach for the disentanglement of vulnerabilities caused by representation learning of DRL agents from those that stem from the sensitivity of the DRL policies to distributional shifts in state transitions. Building on this approach, we propose two RL-based techniques for quantitative benchmarking of adversarial resilience and robustness in DRL policies against perturbations of state transitions. We demonstrate the feasibilit

Research Question

To what extent does the latent action discretization strategy in DiLA improve robustness against visual perception noise in multimodal language-conditioned policies evaluated on CALVIN?

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