Robot path planning using enhanced Q-learning algorithm based on single parameter.

One of the challenging aspects of robot navigation is path planning in a dynamic environment. The Q-learning algorithm is one of the reinforcement learning techniques that can be applied to the path planning of a mobile robot. The vital algorithm for any intelligent mobile robot is path planning. On the other hand, the traditional Q-learning method examines every conceivable state of the robot to choose the optimal path. As a result, this method is very computationally intensive, especially when there is a need to compute a large environment. This study proposes a modified version of the technique for planning robot paths. Using the learning rate (1-α) instead of the certification discount factor (γ), the algorithm became completely dependent on the reliance parameters, making it one of those that depend on a single parameter. This reliance can reduce the number of parameters and increase the algorithm’s execution efficiency. A modified version of Q-learning was investigated with to determine the optimal path planning in several dynamic obstacle environments. Learning efficiency was enhanced by using priority trial replay in the improved Inclined Eight Connection Q-learning Algorithm (I8QA). A simulated environment was used for the suggested method, and it was shown that it can successfully plan optimal paths in dynamic obstacle environments. Overall, Q-learning, a strong and adaptable reinforcement learning method, is utilized for dealing with a wide range of problems. The improvement ratio of path length in the experiment environment is 40.812%, indicating that the I8QA algorithm is more compatible with dynamic environments.