Statistical Inference of the Value Function for Reinforcement Learning in Infinite-Horizon Settings
无限时域强化学习中价值函数的统计推断
Abstract Reinforcement learning is a general technique that allows an agent to learn an optimal policy and interact with an environment in sequential decision-making problems. The goodness of a policy is measured by its value function starting from some initial state. The focus of this paper was to construct confidence intervals (CIs) for a policy’s value in infinite horizon settings where the number of decision points diverges to infinity. We propose to model the…