A parallel network that learns to play backgammon
Artificial Intelligence
Technical Note: \cal Q-Learning
Machine Learning
Artificial Intelligence - Special issue on Robocop: the first step
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Dynamic Programming
Least-squares policy iteration
The Journal of Machine Learning Research
Tree-Based Batch Mode Reinforcement Learning
The Journal of Machine Learning Research
Learning to Control in Operational Space
International Journal of Robotics Research
Learning to Drive a Real Car in 20 Minutes
FBIT '07 Proceedings of the 2007 Frontiers in the Convergence of Bioscience and Information Technologies
A Case Study on Improving Defense Behavior in Soccer Simulation 2D: The NeuroHassle Approach
RoboCup 2008: Robot Soccer World Cup XII
Combining Policy Search with Planning in Multi-agent Cooperation
RoboCup 2008: Robot Soccer World Cup XII
ECML'05 Proceedings of the 16th European conference on Machine Learning
Learning visual representations for perception-action systems
International Journal of Robotics Research
On optimizing interdependent skills: a case study in simulated 3D humanoid robot soccer
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
UT Austin Villa 2011: a champion agent in the RoboCup 3D soccer simulation competition
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Active learning of inverse models with intrinsically motivated goal exploration in robots
Robotics and Autonomous Systems
Engineering Applications of Artificial Intelligence
Reinforcement learning in robotics: A survey
International Journal of Robotics Research
Learning collaborative team behavior from observation
Expert Systems with Applications: An International Journal
Reinforcement learning algorithms with function approximation: Recent advances and applications
Information Sciences: an International Journal
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Batch reinforcement learning methods provide a powerful framework for learning efficiently and effectively in autonomous robots. The paper reviews some recent work of the authors aiming at the successful application of reinforcement learning in a challenging and complex domain. It discusses several variants of the general batch learning framework, particularly tailored to the use of multilayer perceptrons to approximate value functions over continuous state spaces. The batch learning framework is successfully used to learn crucial skills in our soccer-playing robots participating in the RoboCup competitions. This is demonstrated on three different case studies.