Induction: processes of inference, learning, and discovery
Induction: processes of inference, learning, and discovery
Technical Note: \cal Q-Learning
Machine Learning
Reinforcement learning with hidden states
Proceedings of the second international conference on From animals to animats 2 : simulation of adaptive behavior: simulation of adaptive behavior
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Artificial Intelligence Review
Learning to Predict by the Methods of Temporal Differences
Machine Learning
Q-Learning in Continuous State and Action Spaces
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Expertness based cooperative Q-learning
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Online learning control by association and reinforcement
IEEE Transactions on Neural Networks
Learning to trade via direct reinforcement
IEEE Transactions on Neural Networks
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In reinforcement learning, there is no supervisor to critically judge the chosen action at each step. The learning is through a trial-and-error procedure interacting with a dynamic environment. Q-learning is one popular approach to reinforcement learning. It is widely applied to problems with discrete states and actions and usually implemented by a look-up table where each item corresponds to a combination of a state and an action. However, the look-up table implementation of Q-learning fails in problems with continuous state and action space because an exhaustive enumeration of all state-action pairs is impossible. In this paper, an implementation of Q-learning for solving problems with continuous state and action space using SOM-based fuzzy systems is proposed. Simulations of training a robot to complete two different tasks are used to demonstrate the effectiveness of the proposed approach. Reinforcement learning usually is a slow process. In order to accelerate the learning procedure, a hybrid approach which integrates the advantages of the ideas of hierarchical learning and the progressive learning to decompose a complex task into simple elementary tasks is proposed.