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• W567721252 abstract "Value functions are a core component of reinforcement learning systems. The main idea is to to construct a single function approximator V (s; θ) that estimates the long-term reward from any state s, using parameters θ. In this paper we introduce universal value function approximators (UVFAs) V (s, g; θ) that generalise not just over states s but also over goals g. We develop an efficient technique for supervised learning of UVFAs, by factoring observed values into separate embedding vectors for state and goal, and then learning a mapping from s and g to these factored embedding vectors. We show how this technique may be incorporated into a reinforcement learning algorithm that updates the UVFA solely from observed rewards. Finally, we demonstrate that a UVFA can successfully generalise to previously unseen goals." @default.
• W567721252 created "2016-06-24" @default.
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• W567721252 date "2015-07-06" @default.
• W567721252 modified "2023-10-02" @default.
• W567721252 title "Universal Value Function Approximators" @default.
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• W567721252 hasPublicationYear "2015" @default.
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