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

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