A Hybrid Planning in Concurrent Dyna-Q Learning for Multi-agent Systems

• Main Authors: Hung, Tsan-Shun, 洪贊順
• Other Authors: Hwang, Kao-Shing
• Format: Others
• Language: en_US
• Published: 2012
• Online Access: http://ndltd.ncl.edu.tw/handle/67686939437674667921

碩士 === 國立中正大學 === 光機電整合工程研究所 === 100 === Traditional reinforcement learning algorithm, such as Q-learning, is based on one agent and one step learning without a model. In recent years, many have proposed the concepts of multi-agents and using a model for retraining to increase learning efficiency, such as Dyna-Q and multi-agent system. In this thesis, we integrated several algorithms of different domains, applied concepts from different domains in reinforcement learning, and made extensions in compliance with existing concepts such as Dyna-Q and multi-agent system. We added UCB algorithm to reinforce exploration efficiency of agents and shorten the time for virtual environment model establishment. For the virtual environment model of Dyna-Q, we added the concept of image processing to sharpened model. We also proposed a planning algorithm for environmental space paralleling, which can perform parallel computing and accelerate Dyna-Q learning. The concept of prioritized sweeping was integrated to further increase planning efficiency and resource management. After improving and integrating the above algorithms, the concept of GPGPU (General Purpose Computing on Graphics Processing Units) was used for simulation on CUDA (Compute Unified Device Architecture). The simulation was applied for verifying the impact of the above method on learning speed of Dyna-Q.