一个因素化SARSA(λ)激励学习算法
A FACTORED SARSA(λ) ALGORITHM OF REINFORCEMENT LEARNING
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摘要: 基于状态的因素化表达 ,提出了一个新的 SARSA(λ)激励学习算法 .其基本思想是根据状态的特征得出状态相似性启发式 ,再根据该启发式对状态空间进行聚类 ,大大减少了状态空间搜索与计算的复杂度 ,因此比较适用于求解大状态空间的 MDPs问题 .Abstract: Based on the factored representation of a state, a new SARSA( λ ) algorithm is proposed. The main principle of the algorithm is that a heuristics on the state similarities can be gained from the features of the state, and according to the heuristics, the state space is aggregated, significantly reducing the searching and computing complexity for the state space. Therefore the algorithm is a promise for solving large scale MDPs problems which are of a huge state space.
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