Learning Transferable Concepts in Deep Reinforcement Learning

• URL: http://arxiv.org/abs/2005.07870v4
• Date: Tue, 22 Feb 2022 08:08:51 GMT
• Title: Learning Transferable Concepts in Deep Reinforcement Learning
• Authors: Diego Gomez, Nicanor Quijano, Luis Felipe Giraldo
• Abstract summary: We show that learning discrete representations of sensory inputs can provide a high-level abstraction that is common across multiple tasks. In particular, we show that it is possible to learn such representations by self-supervision, following an information theoretic approach. Our method is able to learn concepts in locomotive and optimal control tasks that increase the sample efficiency in both known and unknown tasks.
• Score: 0.7161783472741748
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: While humans and animals learn incrementally during their lifetimes and exploit their experience to solve new tasks, standard deep reinforcement learning methods specialize to solve only one task at a time. As a result, the information they acquire is hardly reusable in new situations. Here, we introduce a new perspective on the problem of leveraging prior knowledge to solve future tasks. We show that learning discrete representations of sensory inputs can provide a high-level abstraction that is common across multiple tasks, thus facilitating the transference of information. In particular, we show that it is possible to learn such representations by self-supervision, following an information theoretic approach. Our method is able to learn concepts in locomotive and optimal control tasks that increase the sample efficiency in both known and unknown tasks, opening a new path to endow artificial agents with generalization abilities.

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