In a Nutshell, the Human Asked for This: Latent Goals for Following Temporal Specifications

Borja G. Leon · Murray Shanahan · Francesco Belardinelli

Keywords: [ deep reinforcement learning ]

[ Abstract ]
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Tue 26 Apr 2:30 a.m. PDT — 4:30 a.m. PDT


We address the problem of building agents whose goal is to learn to execute out-of distribution (OOD) multi-task instructions expressed in temporal logic (TL) by using deep reinforcement learning (DRL). Recent works provided evidence that the agent's neural architecture is a key feature when DRL agents are learning to solve OOD tasks in TL. Yet, the studies on this topic are still in their infancy. In this work, we propose a new deep learning configuration with inductive biases that lead agents to generate latent representations of their current goal, yielding a stronger generalization performance. We use these latent-goal networks within a neuro-symbolic framework that executes multi-task formally-defined instructions and contrast the performance of the proposed neural networks against employing different state-of-the-art (SOTA) architectures when generalizing to unseen instructions in OOD environments.

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