Reinforcement Learning for Internet Applications

Reinforcement learning (RL) has achieved great success in video and board games, in which RL can make improvement through (almost) infinite self-play. However, different from games, in real-world applications,  self-play is either impossible or costly. In this project, our goal is to make RL feasible and successful in real-world applications. Our targeted applications include machine translation, chat bot, and search advertising.