Policy Sharing Using Aggregation Trees for -Learning in a Continuous State and Action Spaces

• Published in: IEEE transactions on cognitive and developmental systems Vol. 12; no. 3; pp. 474 - 485
• Main Authors: Chen, Yu-Jen, Jiang, Wei-Cheng, Ju, Ming-Yi, Hwang, Kao-Shing
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.09.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: <italic xmlns:ali="http://www.niso.org/schemas/ali/1.0/" xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">Q -learning; Approximation; Architecture; Continuity (mathematics); Discretization; Domains; Estimation error; Intelligent agents; Learning; Multi-agent systems; Multiagent system; Multiagent systems; policy sharing; Reinforcement learning; Tree data structures; tree structure; Trees

Q-learning is a generic approach that uses a finite discrete state and an action domain to estimate action values using tabular or function approximation methods. An intelligent agent eventually learns policies from continuous sensory inputs and encodes these environmental inputs onto a discrete sta...