An Improved Tentative Q Learning Algorithm for Robot Learning

摘要

Aiming at the problem of the slow speed of reinforcement learning, a tentative Q learning algorithm is proposed. By improving the number of exploration in each learning iteration and the updating method of Q table, tentative Q learning algorithm accelerates the learning speed and ensures the balance between exploration and exploitation. Finally, the feasibility and effectiveness of the algorithm are proved by the experiment of robot path planning.