Systematic Generalisation through Task Temporal Logic and Deep Reinforcement Learning

Abstract

This paper presents a neuro-symbolic agent that combines deep reinforcement learning (DRL) with temporal logic (TL), and achieves systematic out-of-distribution generalisation in tasks that involve following a formally specified instruction. Specifically, the agent learns general notions of negation and disjunction, and successfully applies them to previously unseen objects without further training. To this end, we also introduce Task Temporal Logic (TTL), a learning-oriented formal language, whose atoms are designed to help the training of a DRL agent targeting systematic generalisation. To validate this combination of logic-based and neural-network techniques, we provide experimental evidence for the kind of neural-network architecture that most enhances the generalisation performance of the agent. Our findings suggest that the right architecture can significatively improve the ability of the agent to generalise in systematic ways, even with abstract operators, such as negation, which previous research have struggled with.

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