We present an algorithm called Optimistic Linear Programming (OLP) for learning to optimize average reward in an irreducible but otherwise unknown Markov decision process (MDP). O...
Planning can often be simplified by decomposing the task into smaller tasks arranged hierarchically. Charlin et al. [4] recently showed that the hierarchy discovery problem can be...
Enhancing the service-oriented architecture paradigm with semantic components is a new field of research and goal of many ongoing projects. The results lead to more powerful web a...
Reinforcement learning problems are commonly tackled with temporal difference methods, which attempt to estimate the agent's optimal value function. In most real-world proble...
Hyperthreaded(HT) and simultaneous multithreaded (SMT) processors are now available in commodity workstations and servers. This technology is designed to increase throughput by ex...
Yun Zhang, Mihai Burcea, Victor Cheng, Ron Ho, Mic...