Activity: Talk or presentation › Talk or presentation at a conference
Reinforcement learning (RL) and more generally sequential decision making deal with problems where the decision maker ('agent') needs to take actions over time. While impressive results have been achieved on challenging domains like Atari, Go, and Starcraft, most of this work relies on neural networks to form their own internal abstractions. However, in many applications, we may be able to exploit some knowledge about the structure of the environment to guide this process. In this talk I will cover some of my work that tries to exploit structure to define effective methods for planning and reinforcement learning.