Rui-Min He

Action-Driven Processes for Continuous-Time Control

This paper shows that minimizing the Kullback-Leibler divergence between a policy-driven true distribution and a reward-driven model distribution for a suitably defined action-driven process is equivalent to maximum entropy reinforcement learning.