Research of profit-sharing reinforcement learning method based on semi-autonomous agent
Yuejin Tan · Computer Engineering and Applications Journal · 2007
We exert the profit-sharing reinforcement learning method into the semi-autonomous agent system,and compare it with the other reinforce learning method--Q-learning.Profit-sharing method is more robust and fit for the dynamic environment which includes many uncertain factors,especially in the partial MDPs(Markov Decision Processes) environment.Facing the semi -autonomous property of the agent,we propose an improving learning method of profit-sharing in the semi-autonomous agent system and test it in a combat simulation environment that finds the safety hidden space in battlefield.At last we contract and analyze these methods to the others.