Artificial Intelligence
Technical Note: \cal Q-Learning
Machine Learning
Qualitative and quantitative simulation: bridging the gap
Artificial Intelligence
Adaptive Behavior
Reinforcement learning with hierarchies of machines
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Between MDPs and semi-MDPs: a framework for temporal abstraction in reinforcement learning
Artificial Intelligence
Reinforcement Learning Using the Stochastic Fuzzy Min–Max Neural Network
Neural Processing Letters
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Neuro-Dynamic Programming
Recent Advances in Hierarchical Reinforcement Learning
Discrete Event Dynamic Systems
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Applications of the self-organising map to reinforcement learning
Neural Networks - New developments in self-organizing maps
Hierarchical reinforcement learning with the MAXQ value function decomposition
Journal of Artificial Intelligence Research
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Quantum reinforcement learning
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part II
An autonomous mobile robot based on quantum algorithm
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
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New representation and computation mechanisms are key approaches for learning problems with incomplete information or in large probabilistic environments. In this paper, traditional reinforcement learning (RL) methods are combined with grey theory and a novel grey reinforcement learning (GRL) framework is proposed to solve complex problems with incomplete information. Typical example of mobile robot navigation is given out to evaluate the performance and practicability of GRL. Related issues are also briefly discussed.