Automatic programming of behavior-based robots using reinforcement learning
Artificial Intelligence
Practical Issues in Temporal Difference Learning
Machine Learning
Feature-based methods for large scale dynamic programming
Machine Learning - Special issue on reinforcement learning
Lazy learning
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Markov Decision Processes: Discrete Stochastic Dynamic Programming
Neuro-Dynamic Programming
Robot Teams: From Diversity to Polymorphism
Robot Teams: From Diversity to Polymorphism
Multiagent Systems: A Survey from a Machine Learning Perspective
Autonomous Robots
Algorithms for Inverse Reinforcement Learning
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
VQQL. Applying Vector Quantization to Reinforcement Learning
RoboCup-99: Robot Soccer World Cup III
Dynamic Programming
Making reinforcement learning work on real robots
Making reinforcement learning work on real robots
Evolutionary Design of Nearest Prototype Classifiers
Journal of Heuristics
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
An Adaptable Oscillator-Based Controller for Autonomous Robots
Journal of Intelligent and Robotic Systems
Information Sciences: an International Journal
A Human-Robot Collaborative Reinforcement Learning Algorithm
Journal of Intelligent and Robotic Systems
Adaptive multi-robot team reconfiguration using a policy-reuse reinforcement learning approach
AAMAS'11 Proceedings of the 10th international conference on Advanced Agent Technology
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Reinforcement learning has been widely applied to solve a diverse set of learning tasks, from board games to robot behaviours. In some of them, results have been very successful, but some tasks present several characteristics that make the application of reinforcement learning harder to define. One of these areas is multi-robot learning, which has two important problems. The first is credit assignment, or how to define the reinforcement signal to each robot belonging to a cooperative team depending on the results achieved by the whole team. The second one is working with large domains, where the amount of data can be large and different in each moment of a learning step. This paper studies both issues in a multi-robot environment, showing that introducing domain knowledge and machine learning algorithms can be combined to achieve successful cooperative behaviours.