Control in Stochastic Environment with Delays: A Model-based Reinforcement Learning Approach

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. …

control cs.lg cs.sy eess.sy embed environment environments feedback optimization paper planning policy reinforcement reinforcement learning risk show stochastic