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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 .

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A Fuzzy approach to backward movement control for trucks in a dynamic environment is presented in this paper. The approach is then extended and employed for conditions where Obstacles randomly are placed on the truck pathway. In the first case, Obstacles randomly are assumed to be fixed, while the second condition includes moving Obstacles randomly through which the truck should be directed toward the parking dock. The method is designed in a way to be used in conditions with infinite number of Obstacles randomly at arbitrary places. In any case, to find the parking dock, the truck movement must be adapted to that of Obstacles randomly. In the present paper, two separate fuzzy controllers are used for directing the truck: one for finding the target, and the other for avoiding the Obstacles randomly. While there is no Obstacles randomly around, the target finder controller is in use; and in the cases where the truck gets close to Obstacles randomly the Obstacles randomly avoider controller is activated. The proposed method is employed for parking a truck model through fixed and moving Obstacles randomly.