Location: H1, room 205; digital participation (link shared via e-mail)<\/p>\n\n\n\n
Andreas Fink (HSU<\/abbr>):<\/strong> HPC for Solving Combinatorial Optimization Problems in Logistics: Challenges and Examples<\/p>\n\n\n\n While HPC is established in engineering and science, this is not yet the case for solving NP-hard combinatorial optimization problems in business administration (e.g. in logistics). Existing optimization methods for such problems often have a sequential flow logic and thus must be adapted. We will discuss if and how mixed-integer mathematical optimization methods and solvers are already able to exploit parallel computing power and we will consider the parallelization of (meta-)heuristic methods.<\/p>\n\n\n\n Benedikt Hein (HSU<\/abbr>):<\/strong> Distributed Deep Reinforcement Learning: How a hundred years of experience can be gathered in one day<\/p>\n\n\n\n Over the past decade, Deep Reinforcement Learning has received considerable attention in Artificial Intelligence research. Successful training in Deep Reinforcement Learning generally requires millions of interactions with a simulated environment. The massive parallelization of simulated training environments recently enabled the training of an outstanding game AI for the computer game DOTA. This talk presents the techniques, principles and goals of this kind of parallelization.<\/p>\n\n\n\n
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