Reinforcement learning with value advice

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Mayank Daswani, Peter Sunehag, Marcus Hutter ;
Proceedings of the Sixth Asian Conference on Machine Learning, PMLR 39:299-314, 2015.

Abstract

The problem we consider in this paper is reinforcement learning with value advice. In this setting, the agent is given limited access to an oracle that can tell it the expected return (value) of any state-action pair with respect to the optimal policy. The agent must use this value to learn an explicit policy that performs well in the environment. We provide an algorithm called RLAdvice, based on the imitation learning algorithm DAgger. We illustrate the effectiveness of this method in the Arcade Learning Environment on three different games, using value estimates from UCT as advice.

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