An Intelligent System For Parking Trailer In Presence Of Fixed And Moving Obstacles Using Reinforcement Learning And Fuzzy Logic

Abstract

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 .

Auto-Scaling Approach for Cloud based Mobile Learning Applications

“…This algorithm is good for dynamic workloads because of its capabilities for self-adapting and self-learning. M. Sharafi et al [28] combine an RL algorithm (SARSA learning) with fuzzified actions. They test their proposed method by simulation using MATLAB and show that this algorithm is efficient for a dynamic workload.…”