Research on deep reinforcement learning basketball robot shooting skills improvement based on end to end architecture and multi-modal perception.

Abstract

Introduction: In the realm of basketball, refining shooting skills and decision-making levels using intelligent agents has garnered significant interest. This study addresses the challenge by introducing an innovative framework that combines multi-modal perception and deep reinforcement learning. The goal is to create basketball robots capable of executing precise shots and informed choices by effectively integrating sensory inputs and learned strategies.

Methods: The proposed approach consists of three main components: multi-modal perception, deep reinforcement learning, and end-to-end architecture. Multi-modal perception leverages the multi-head attention mechanism (MATT) to merge visual, motion, and distance cues for a holistic perception of the basketball scenario. The deep reinforcement learning framework utilizes the Deep Q-Network (DQN) algorithm, enabling the robots to learn optimal shooting strategies over iterative interactions with the environment. The end-to-end architecture connects these components, allowing seamless integration of perception and decision-making processes.

Results: The experiments conducted demonstrate the effectiveness of the proposed approach. Basketball robots equipped with multi-modal perception and deep reinforcement learning exhibit improved shooting accuracy and enhanced decision-making abilities. The multi-head attention mechanism enhances the robots' perception of complex scenes, leading to more accurate shooting decisions. The application of the DQN algorithm results in gradual skill improvement and strategic optimization through interaction with the environment.

Discussion: The integration of multi-modal perception and deep reinforcement learning within an end-to-end architecture presents a promising avenue for advancing basketball robot training and performance. The ability to fuse diverse sensory inputs and learned strategies empowers robots to make informed decisions and execute accurate shots. The research not only contributes to the field of robotics but also has potential implications for human basketball training and coaching methodologies.