Temporal difference learning and TD-Gammon
Communications of the ACM
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Kernel-Based Reinforcement Learning
Machine Learning
Approximate solutions to markov decision processes
Approximate solutions to markov decision processes
Tree-Based Batch Mode Reinforcement Learning
The Journal of Machine Learning Research
Machine Learning
Batch reinforcement learning in a complex domain
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Reinforcement learning: a survey
Journal of Artificial Intelligence Research
Improving management of Anemia in end stage renal disease using reinforcement learning
IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
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This paper highlights the crucial role that modern machine learning techniques can play in the optimization of treatment strategies for patients with chronic disorders. In particular, we focus on the task of optimizing a deep-brain stimulation strategy for the treatment of epilepsy. The challenge is to choose which stimulation action to apply, as a function of the observed EEG signal, so as to minimize the frequency and duration of seizures. We apply recent techniques from the reinforcement learning literature--namely fitted Q-iteration and extremely randomized trees--to learn an optimal stimulation policy using labeled training data from animal brain tissues. Our result, show that these methods are an effective means of reducing tile incidence of seizures, while also minimizing the amount ot stimulation applied. If these results Carry over to the human model of epilepsy, the impact for patients will be substantial.