基于深度强化学习的曲棍球击球学习控制
Learning Control for Air Hockey Striking using Deep Reinforcement Learning
摘要：
我们认为学习控制策略的机器人机制，在空气曲棍球比赛中打冰球的任务。控制信号是对机器人电机的直接命令。我们采用了一个无模型的深度强化学习框架来学习准确击球的运动技能，以便得分。我们对标准学习方案提出了某些改进，使深度Q-学习算法在可能失败时变得可行。我们的改进包括将先验知识集成到学习方案中，并考虑经验重放缓冲区中样本分布的变化。最后，我们提出了我们的模拟结果，有针对性的打击，这表明成功的学习这项任务，并提高算法的稳定性，由于所提出的修改。
英文摘要：
We consider the task of learning control policies for a robotic mechanism striking a puck in an air hockey game. The control signal is a direct command to the robot's motors. We employ a model free deep reinforcement learning framework to learn the motoric skills of striking the puck accurately in order to score. We propose certain improvements to the standard learning scheme which make the deep Q-learning algorithm feasible when it might otherwise fail. Our improvements include integrating prior knowledge into the learning scheme, and accounting for the changing distribution of samples in the experience replay buffer. Finally we present our simulation results for aimed striking which demonstrate the successful learning of this task, and the improvement in algorithm stability due to the proposed modifications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
