Q-map: a Convolutional Approach for Goal-Oriented Reinforcement Learning

ICLR 2019 Fabio PardoVitaly LevdikPetar Kormushev

Goal-oriented learning has become a core concept in reinforcement learning (RL), extending the reward signal as a sole way to define tasks. However, as parameterizing value functions with goals increases the learning complexity, efficiently reusing past experience to update estimates towards several goals at once becomes desirable but usually requires independent updates per goal... (read more)

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