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Meta Dynamic Programming
Pierluca D'Oro
Event URL: https://www.loreggia.eu/MetacogNeurIPS2021/MADL2021_paper_8.pdf »
To accelerate the pace at which they acquire new information, reinforcement learning algorithms can select which data to use first for training. In this paper, we outline a general methodology to perform this selection process, hinting at a generation of agents which deeply think about their current and future learning state while selecting their training data. In the context of prioritization methods for asynchronous dynamic programming, we propose a meta-level technique for state selection. We show that the method, called meta dynamic programming, together with its approximations, can provide promising performance improvements while being grounded on a theoretically sound metacognitive formalization.
Author Information
Pierluca D'Oro (Mila)
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