Control in Stochastic Environment with Delays: A Model-based
  Reinforcement Learning Approach
Zhiyuan Yao Ionut Florescu Chihoon Lee
In this paper we are introducing a new reinforcement learning method for
control problems in environments with delayed feedback. Specifically, our
method employs stochastic planning, versus previous methods that used
deterministic planning. This allows us to embed risk preference in the policy
optimization problem. We show that this formulation can recover the optimal
policy for problems with deterministic transitions. We contrast our policy with
two prior methods from literature. We apply the methodology to simple tasks to
understand its features. Then, we compare the performance of the methods in
controlling multiple Atari games.