An Intelligent System for Parking Trailer using Reinforcement Learning and Fuzzy Logic
In examples of reinforcement learning where state space is continuous, it seems impossible to use reference tables to store value-action .In these problems a method is required for value estimation for each state-action pair .The inputs to this estimation system are (characteristics of) state variables which reflect the status of agent in the environment .The system can be either linear of nonlinear .For each member in set of actions of an agent, there exists an estimation system which determines state value for the action . On the other hand, in most real world problems, just as the state space is continuous, so is the action space for an agent .In these cases, fuzzy systems may provide a useful solution in selection of final action from action space .In this paper we intend to combine reinforcement learning algorithm with fuzzified actions and state space along with a linear estimation system into an intelligent systems for parking Trailers in cases where both state and action spaces are continuous .Finally, the successful performance of the proposed algorithm is shown through simulations on trailer parking problem .
Reinforcement Learning Fuzzy Systems Trailer Parking Problem SARSA Algorithm
M. Sharafi Javad Rohani A.zare
University - Gonabad Branch Member of young researchers club Gonabad-Iran University Gonabad Branch University - Gonabad Branch Gonabad-Iran
国际会议
2010 International Conference on Measurement and Control Engineering(2010年IEEE测量与控制工程国际会议 ICMCE2010)
成都
英文
98-102
2010-11-16（万方平台首次上网日期，不代表论文的发表时间）