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title
NeurIPS 2019 Optimization Foundations of Reinforcement Learning Workshop
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Workshop at NeurIPS 2019, Dec 14th, 2019
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Optimization Foundation of Reinforcement Learning
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2025-12-18 14:28:48
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NeurIPS 2019 Optimization Foundations of Reinforcement Learning Workshop | Optimization Foundation of Reinforcement Learning NeurIPS 2019 Optimization Foundations of Reinforcement Learning Workshop Workshop at NeurIPS 2019, Dec 14th, 2019 West Ballroom A, Vancouver Convention Center, Vancouver, Canada Home Schedule Awards Call For Papers Accepted Papers Background Dynamic programming (DP) based algorithms, which apply various forms of the Bellman operator, dominate the literature on model-free reinforcement learning (RL). While DP is powerful, the value function estimate can oscillate or even diverge when function approximation is introduced with off-policy data, except in special cases. This problem has been well-known for decades (referred to as the deadly triad in the literature), and has remained a critical open fundamental problem in RL. More recently, the community witnessed a fast-growing trend that frames RL problems as well-posed optimization problems, in...
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