Technical Note: \cal Q-Learning
Machine Learning
Adaptive choice of grid and time in reinforcement learning
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
A Necessary Condition of Convergence for Reinforcement Learning with Function Approximation
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
Ego-Motion Estimation and Collision Detection for Omnidirectional Robots
RoboCup 2006: Robot Soccer World Cup X
Variable resolution discretization for high-accuracy solutions of optimal control problems
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Policy Gradients with Parameter-Based Exploration for Control
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Roles, Positionings and Set Plays to Coordinate a RoboCup MSL Team
EPIA '09 Proceedings of the 14th Portuguese Conference on Artificial Intelligence: Progress in Artificial Intelligence
Cognitive concepts in autonomous soccer playing robots
Cognitive Systems Research
Ball interception behaviour in robotic soccer
Robot Soccer World Cup XV
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In this paper, we show how reinforcement learning can be applied to real robots to achieve optimal robot behavior. As example, we enable an autonomous soccer robot to learn intercepting a rolling ball. Main focus is on how to adapt the Q-learning algorithm to the needs of learning strategies for real robots and how to transfer strategies learned in simulation onto real robots.