Learning Behaviorally Grounded State Representations for Reinforcement Learning Agents

Abstract

The learning and reasoning capabilities of biological systems by far exceed those of robots and artificial agents. Part of this stems from their ability to efficiently learn behavioral skills and increasingly complex, symbolic representations that capture the important aspects of their environment. This paper presents an autonomous learning approach by… (More)

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