Research on path planning of 4-link robot based on optimized Q-learning algorithm

In Q-learning algorithm, the balance between exploration and exploitation is crucial for the success of the entire algorithm. If the algorithm is too biased towards exploration, it may waste too much time on unnecessary paths, while if the algorithm is too biased towards exploitation, it may miss better paths. Ensuring the balance between exploration and exploitation in Q-learning algorithms requires comprehensive consideration of various aspects of the algorithm, including learning rate, state space representation, reward function design, policy selection, and addition of random noise. This paper proposes and verifies the construction of the state and action sets for a three-degree- of-freedom robotic arm within a specified space, where the reward values are reasonably set, and Q-learning with an introduced exploration factor is used as an improvement to prevent the robotic arm from insufficient exploration in action selection, which can lead to the algorithm failing to find a better strategy. The results of experiment illustrate that introducing the exploration factor can significantly enhance the convergence speed of the algorithm.