Learning Control for Air Hockey Striking using Deep Reinforcement Learning
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Deep Reinforcement LearningQ-Learningreinforcement-learningReinforcement LearningReinforcement Learning (RL)Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Learning to Play Air Hockey with Model-Based Deep Reinforcement Learning
In the context of addressing the Robot Air Hockey Challenge 2023, we investigate the applicability of model-based deep reinforcement learning to acquire a policy capable of autonomously playing air hockey. Our agents lea…
Deep Reinforcement LearningPositionRobot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning
Reinforcement Learning is a promising tool for learning complex policies even in fast-moving and object-interactive domains where human teleoperation or hard-coded policies might fail. To effectively reflect this challen…
Offline RLLearning Diverse Robot Striking Motions with Diffusion Models and Kinematically Constrained Gradient Guidance
Advances in robot learning have enabled robots to generate skills for a variety of tasks. Yet, robot learning is typically sample inefficient, struggles to learn from data sources exhibiting varied behaviors, and does no…
Imitation LearningAutomatic Generation of Ice Hockey Defensive Motion via Coverage Control and Control Barrier Functions
A successful defensive strategy in ice hockey games is often designed empirically by an experienced professional. The majority of previous work on automating the strategy focuses on analyzing spatial data to decide the m…
validLocalization of Ice-Rink for Broadcast Hockey Videos
In this work, an automatic and simple framework for hockey ice-rink localization from broadcast videos is introduced. First, video is broken into video-shots by a hierarchical partitioning of the video frames, and thresh…
Homography Estimation