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Conference Papers Year : 2020

DEEP SETS FOR GENERALIZATION IN RL

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Tristan Karch
Cédric Colas
Pierre-Yves Oudeyer

Abstract

This paper investigates the idea of encoding object-centered representations in the design of the reward function and policy architectures of a language-guided reinforcement learning agent. This is done using a combination of object-wise permutation invariant networks inspired from Deep Sets and gated-attention mechanisms. In a 2D procedurally-generated world where agents targeting goals in natural language navigate and interact with objects, we show that these architectures demonstrate strong generalization capacities to out-of-distribution goals. We study the generalization to varying numbers of objects at test time and further extend the object-centered architectures to goals involving relational reasoning.
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hal-03120669 , version 1 (26-01-2021)

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Tristan Karch, Cédric Colas, Laetitia Teodorescu, Clément Moulin-Frier, Pierre-Yves Oudeyer. DEEP SETS FOR GENERALIZATION IN RL. Beyond Tabula Rasa in Reinforcement Learning: agents that remember adapt and generalize, Workshop at ICLR, Apr 2021, Addis Ababa, Ethiopia. ⟨hal-03120669⟩

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