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Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies
January/February 2007 (vol. 11 no. 1)
pp. 22-30
Gerald Tesauro, IBM T.J.Watson Research Center
Reinforcement learning (RL) is a promising new approach for automatically developing effective policies for real-time self-* management. RL has the potential to achieve superior performance to traditional methods while requiring less built-in domain knowledge. Several case studies from real and simulated systems-management applications demonstrate RL's promises and challenges. These studies show that standard online RL can learn effective policies in feasible training times. Moreover, a Hybrid RL approach can profit from any knowledge contained in an existing policy by training on the policy's observable behavior without needing to interface directly to such knowledge.
Index Terms:
autonomic computing, reinforcement learning, systems management, training
Citation:
Gerald Tesauro, "Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies," IEEE Internet Computing, vol. 11, no. 1, pp. 22-30, Jan.-Feb. 2007, doi:10.1109/MIC.2007.21
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