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DPO: Differential reinforcement learning with application to optimal configuration search
April 25, 2024, 7:42 p.m. | Chandrajit Bajaj, Minh Nguyen
cs.LG updates on arXiv.org arxiv.org
Abstract: Reinforcement learning (RL) with continuous state and action spaces remains one of the most challenging problems within the field. Most current learning methods focus on integral identities such as value functions to derive an optimal strategy for the learning agent. In this paper, we instead study the dual form of the original RL formulation to propose the first differential RL framework that can handle settings with limited training samples and short-length episodes. Our approach introduces …
abstract agent application arxiv continuous cs.ai cs.lg current differential dpo focus functions integral math.oc math.st paper reinforcement reinforcement learning search spaces state stat.th strategy type value
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