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An agent's ability to leverage past experience is critical for efficientlysolving new tasks. Approximate solutions for new tasks can be obtained frompreviously derived value functions, as demonstrated by research on transferlearning, curriculum learning, and compositionality. However, prior work hasprimarily focused on using value functions to obtain zero-shot approximationsfor solutions to a new task. In this work, we show how an arbitraryapproximation for the value function can be used to derive double-sided boundson the optimal value function of interest. We further extend the framework witherror analysis for continuous state and action spaces. The derived results leadto new approaches for clipping during training which we validate numerically insimple domains.

Leveraging Prior Knowledge in Reinforcement Learning via Double-Sided Bounds on the Value Function