Online Reinforcement Learning for Dynamic Multimedia Systems

Online Autonomous Layered Learning in Dynamic Multimedia Systems

In our previous paper, we proposed a systematic cross-layer framework for dynamic multimedia systems, which allows each layer (i.e. application, operating system, and hardware) to make autonomous and foresighted decisions that maximize the system’s long-term performance. The proposed solution solves the cross-layer optimization offline, under the assumption that the multimedia system’s probabil...

Reinforcement Learning in Online Stock Trading Systems

Applications of Machine Learning (ML) to stock market analysis include Portfolio Optimization, Investment Strategy Determination, and Market Risk Analysis. This paper focuses on the problem of Investment Strategy Determination through the use of reinforcement learning techniques. Four techniques, two based on Recurrent Reinforcement Learning (RLL) and two based on Q-learning, were utilized. Q-l...

Decentralized Reinforcement Learning for the Online Optimization of Distributed Systems

Reinforcement Learning for Dynamic Channel Allocation in Cellular Telephone Systems

In cellular telephone systems, an important problem is to dynamically allocate the communication resource (channels) so as to maximize service in a stochastic caller environment. This problem is naturally formulated as a dynamic programming problem and we use a reinforcement learning (RL) method to find dynamic channel allocation policies that are better than previous heuristic solutions. The p...

Learning to control dynamic systems via associative reinforcement learning

and the lack of explicit instructional information about how to perform a given control task. Under these circumstances, techniques developed by arti cial intelligence researchers for \learning from examples," including the \supervised learning" techniques studied by neural network researchers, are not directly applicable because these techniques are based on the availability of training inform...