A Reinforcement Learning Algorithm in Partially Observable Environments Using Short-Term Memory

Abstract:
We describe a Reinforcement Learning algorithm for partially observable environments using short-term memory, which we call BLHT. Since BLHT learns a stochastic model based on Bayesian Learning, the overfitting problem is reasonably solved. Moreover, BLHT has an efficient implementation. This paper shows that the model learned by BLHT converges to one which provides the most accurate predictions of percepts and rewards, given short-term memory.