A Value Function Basis for Nexting and Multi-step Prediction

Humans and animals continuously make short-term cumulative predictions about their sensory- input stream, an ability referred to by psychologists as nexting. This ability has been recreated in a mobile robot by learning thousands of value function predictions in parallel. In practice, however, there are limita- tions on the number of things that an autonomous agent can learned. In this paper, we investigate inferring new predictions from a minimal set of learned General Value Functions. We show that linearly weighting such a collection of value function predictions enables us to make accurate multi-step predictions, and pro- vide a closed-form solution to estimate this linear weighting. Similarly, we provide a closed-form solution to estimate value functions with arbitrary discount parameters γ.

Authors

• Andrew Jacobsen
• Vincent Liu RBC Borealis
• Adam White
• Martha White University of Alberta

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