Online and Reinforcement learning - DIKU

Beskrivelse

Online and reinforcement learning break out of the static realm and move into the realm of perpetual cycle of getting new information, analysing it, and executing actions based on the updated estimation of reality. We consider agents (computer programs, robots, living beings) learning based on interactions with (real or simulated) environments. Examples include problems like repeated investment in the stock market, spam filtering, online advertising, online routing, medical treatments, games, and robotics. It allows to model a much richer range of problems, including problems with limited feedback, problems with delayed feedback, and even adversarial problems, where the environment deliberately acts against the algorithm (as, for example, in chess or spam filtering). At the same time it stimulates the development of fascinating mathematical tools for developing and analyzing algorithms for these problems.

Københavns Universitet