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Warehouse data center is very large scale and complex, which constains tens of thousands servers and accomodates various applications. What's more important, energy consumption has risen to a critical point. Scheduling needs to maintain performance and reduce energy consumption as much as possible. Previous researches have proposed RL (reinforcement learning) as a solution. These approaches have reduced...
Robots controlled by Reinforcement Learning (RL) are still rare. A core challenge to the application of RL to robotic systems is to learn despite the existence of control delay - the delay between measuring a system's state and acting upon it. Control delay is always present in real systems. In this work, we present two novel temporal difference (TD) learning algorithms for problems with control delay...
Biological systems tend to perform a range of tasks of extreme variability with extraordinary efficiency. It has been argued that a plausible scenario for achieving such versatility is explicitly learning a forward model. We perform a set of experiments using the original and a modified version of a classic reinforcement learning task, the mountain car problem, using a number of agents that encode...
A developing agent needs to explore to learn about the world and learn good behaviors. In many real world tasks, this exploration can take far too long, and the agent must make decisions about which states to explore, and which states not to explore. Bayesian methods attempt to address this problem, but take too much computation time to run in reasonably sized domains. In this paper, we present TEXPLORE,...
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