A Theoretical Connection Between Statistical Physics and Reinforcement Learning
20 Jan 2021This is a reading note centering around paper Jad Rahme and Ryan Adams (2019) A Theoretical Connection Between Statistical Physics and Reinforcement Learning. I would rather call it a framework to understand RL centering partition function. It is a very heuristic work (but again, seems not very useful at the moment), as we long have the feeling that RL and statistical mechanics deal with the similar issue about integrating all phase space. So this paper provide a good example about how we can start from that intuition and establish more. Similarly, I could also imagine that there is a way to understand the RL in a path intergral framework.
TBC