Almost Optimal Model-Free Reinforcement Learning via Reference-Advantage Decomposition

21 Apr 2020Zihan ZhangYuan ZhouXiangyang Ji

We study the reinforcement learning problem in the setting of finite-horizon episodic Markov Decision Processes (MDPs) with $S$ states, $A$ actions, and episode length $H$. We propose a model-free algorithm UCB-Advantage and prove that it achieves $\tilde{O}(\sqrt{H^2SAT})$ regret where $T = KH$ and $K$ is the number of episodes to play... (read more)

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