Reinforcement Learning Beyond Optization

Dec 14, 2019

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The reinforcement learning problem is often framed as one of quickly optimizing an uncertain Markov decision process. This formulation has led to substantial insight and progress in algorithms and theory. However, this perspective is limiting and can also give rise to poor algorithm designs. I will discuss this issue and how it is addressed by popular reinforcement learning algorithms.

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Neural Information Processing Systems (NeurIPS) is a multi-track machine learning and computational neuroscience conference that includes invited talks, demonstrations, symposia and oral and poster presentations of refereed papers. Following the conference, there are workshops which provide a less formal setting.

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