Research on deep reinforcement learning basketball robot shooting skills improvement based on end to end architecture and multi-modal perception - Zhang, Jun; Tao, Dayong (Frontiers in Neurorobotics, 2023)
In the field of basketball, how to improve shooting skills and decision-making level with the help of intelligent agents has always been a topic of great interest. The basketball robot's advanced personalized training, actual combat simulation and data analysis functions provide players with precise technical guidance and tactical optimization, and at the same time assist the coach in scientific management of the team, realizing a new entertainment and interactive experience, bringing modern basketball training and competitions of great practical significance. This research aims to improve the shooting skills of basketball robots by combining the end-to-end architecture (ete), multi-modal perception and deep reinforcement learning methods to integrate the perception and decision-making processes into a unified learning system, so as to achieve More efficient and precise shooting behavior. In terms of multi-modal perception, we introduce the multi-head attention mechanism (matt), which enables the basketball robot to understand the surrounding environment and the state of the basketball more comprehensively by simultaneously fusing perception information such as vision, motion and distance. This multi-modal perception method enhances the robot's ability to perceive complex scenes and improves the accuracy of shooting decisions. In terms of deep reinforcement learning, we adopted the DQN algorithm and applied it to the improvement of shooting skills of basketball robots. Through deep reinforcement learning, basketball robots can learn better strategies from the interaction with the environment, gradually improve shooting skills and optimize shooting effects. The introduction of the DQN algorithm enables the robot to learn and master complex shooting movements more efficiently. In the experimental part, we verify the effectiveness of the proposed method through a large number of simulation experiments and real scene tests. Our work lays the foundation for further exploration of the application of robots in complex environments, and provides useful inspiration for the research of deep learning in the field of agent control. In the future, we can continue to expand 1 Sample et al.the application of multi-modal perception and deep reinforcement learning in other robot tasks, and further promote the development and application of agents in the real world.