March 13, 2024, 4:41 a.m. | David Cheikhi, Daniel Russo

cs.LG updates on arXiv.org arxiv.org

arXiv:2403.07136v1 Announce Type: new
Abstract: Identifying the trade-offs between model-based and model-free methods is a central question in reinforcement learning. Value-based methods offer substantial computational advantages and are sometimes just as statistically efficient as model-based methods. However, focusing on the core problem of policy evaluation, we show information about the transition dynamics may be impossible to represent in the space of value functions. We explore this through a series of case studies focused on structures that arises in many important …

abstract advantages arxiv computational core cs.ai cs.lg efficiency evaluation free functions however policy power question reinforcement reinforcement learning statistical stat.ml trade type value

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