Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning

• URL: http://arxiv.org/abs/2310.00229v4
• Date: Sat, 16 Mar 2024 18:59:23 GMT
• Title: Consciousness-Inspired Spatio-Temporal Abstractions for Better Generalization in Reinforcement Learning
• Authors: Mingde Zhao, Safa Alver, Harm van Seijen, Romain Laroche, Doina Precup, Yoshua Bengio,
• Abstract summary: Skipper is a model-based reinforcement learning framework. It automatically generalizes the task given into smaller, more manageable subtasks. It enables sparse decision-making and focused abstractions on the relevant parts of the environment.
• Score: 83.41487567765871
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Inspired by human conscious planning, we propose Skipper, a model-based reinforcement learning framework utilizing spatio-temporal abstractions to generalize better in novel situations. It automatically decomposes the given task into smaller, more manageable subtasks, and thus enables sparse decision-making and focused computation on the relevant parts of the environment. The decomposition relies on the extraction of an abstracted proxy problem represented as a directed graph, in which vertices and edges are learned end-to-end from hindsight. Our theoretical analyses provide performance guarantees under appropriate assumptions and establish where our approach is expected to be helpful. Generalization-focused experiments validate Skipper's significant advantage in zero-shot generalization, compared to some existing state-of-the-art hierarchical planning methods.

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