# Learning Control for Air Hockey Striking using Deep Reinforcement Learning

arXiv: https://arxiv.org/abs/1702.08074 · HTML: https://mlsanity.com/paper/1702.08074/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Feb 2017.

## Built on by (most cited first, 3 of 3)

- [Robot Air Hockey: A Manipulation Testbed for Robot Learning with Reinforcement Learning](https://mlsanity.com/paper/2405.03113/) (2024)
- [Control-Optimized Deep Reinforcement Learning for Artificially Intelligent Autonomous Systems](https://mlsanity.com/paper/2507.00268/) (2025)
- [From Kinematics to Dynamics: Learning to Refine Hybrid Plans for Physically Feasible Execution](https://mlsanity.com/paper/2604.12474/) (2026)

## Abstract (arXiv)

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.