$γ$-Regret for Non-Episodic Reinforcement Learning

12 Feb 2020 Shuang Liu Hao Su

Reinforcement learning (RL) has traditionally been understood from an episodic perspective; the concept of non-episodic RL, where there is no restart and therefore no reliable recovery, remains elusive. A fundamental question in non-episodic RL is how to measure the performance of a learner and derive algorithms to maximize such performance... (read more)

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Methods used in the Paper


METHOD TYPE
Double Q-learning
Off-Policy TD Control
Q-Learning
Off-Policy TD Control