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

29 Sept 2021, 00:31 (edited 02 Mar 2022)ICLR 2022 PosterReaders: Everyone
  • Keywords: Deep Reinforcement Learning, Out-Of-Distribution Generalisation, Temporal Logic
  • Abstract: 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.
  • One-sentence Summary: Inducing architectures to generate low-dimensional representations of their current goal processing observations and instructions together yields stronger out-of-distribution generalisation
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