An Advance Q Learning (AQL) Approach for Path Planning and Obstacle Avoidance of a Mobile Robot

Abstract

The goal of this paper is to improve the performance of the well known Q learning algorithm, the robust technique of Machine learning to facilitate path planning in an environment. Until this time the Q learning algorithms like Classical Q learning(CQL)algorithm and Improved Q learning (IQL) algorithm deal with an environment without obstacles, while in a real environment an agent has to face obstacles very frequently. Hence this paper considers an environment with number of obstacles and has coined a new parameter, called ‘immediate penalty’ due to collision with an obstacle. Further the proposed technique has replaced the scalar ‘immediate reward’ function by ‘effective immediate reward’ function which consists of two fuzzy parameters named as, ‘immediate reward’ and ‘immediate penalty’. The fuzzification of these two important parameters not only improves the learning technique, it also strikes a balance between exploration and exploitation, the most challenging problem of Reinforcement Learning. The proposed algorithm stores the Q value for the best possible action at a state; as well it saves significant path planning time by suggesting the best action to adopt at each state to move to the next state. Eventually, the agent becomes more intelligent as it can smartly plan a collision free path avoiding obstacles from distance. The validation of the algorithm is studied through computer simulation in a maze like environment and also on KheperaII platform in real time. An analysis reveals that the Q Table, obtained by the proposed Advanced Q learning (AQL) algorithm, when used for path-planning application of mobile robots outperforms the classical and improved Q-learning.