Duality in Deep Reinforcement Learning--Theory
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Jie Bai, Jianfei Li, Zihao Luo, Yaobing Wang, Li Liu
More and more deeper reinforcement learning algorithms have been proposed and demonstrated on a series of decision-making domains. However, little research has been hammered at algorithm extraction. With duality in deep reinforcement learning substantially summarized, we propose a conceptually simple framework for deep reinforcement learning based on duality. Then, we propose the dual method of prioritized sampling: prioritized learning. Finally, we give the formula and analysis for the duality with priority. The algorithm implementation and experiment will be put on Part II-Implementation.
Deep Reinforcement Learning, Duality, Prioritized Sampling, Prioritized Learning