Reinforcement Learning in Autonomic Computing: A Manifesto and Case Studies

These studies show that standard online RL can learn effective policies in feasible training times, and 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.

Abstract

Reinforcement learning is a promising new approach for automatically developing effective policies for real-time self-management. RL can 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