Foundations of Reinforcement Learning and Control: Connections and Perspectives
Abstract
Despite rapid advances in machine learning, solving large-scale stochastic dynamic programming problems remains a significant challenge. The combination of neural networks with RL has opened new avenues for algorithm design, but the lack of theoretical guarantees of these approaches hinders their applicability to high-stake problems traditionally addressed using control theory, such as online supply chain optimization, industrial automation, and adaptive transportation systems. This workshop focuses on recent advances in developing a learning theory of decision (control) systems, that builds on techniques and concepts from two communities that have had limited interactions despite their shared target: reinforcement learning and control theory.
Video
Schedule
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12:00 AM
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12:40 AM
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1:35 AM
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4:15 AM
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5:00 AM
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6:30 AM
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