Evolutionary Search, Stochastic Policies with Memory, and Reinforcement Learning with Hidden State
Matthew Glickman, Katia P. Sycara · 2001
Reinforcement Learning (RL) problems with hidden state present significant obstacles to prevailing RL methods. In this paper, we present experiments conducted using a straightforward approach to solving such problems that trains artificial neural networks with recurrent connections to represent action policies using evolutionary search.