Steady State Analysis of Episodic Reinforcement Learning

Abstract

Reinforcement Learning (RL) tasks generally divide into two kinds: continual learning and episodic learning. The concept of steady state has played a foundational role in the continual setting, where unique steady-state distribution is typically presumed to exist in the task being studied, which enables principled conceptual framework as well as efficient data collection method for continual RL algorithms. On the other hand, the concept of steady state has been widely considered irrelevant for episodic RL tasks, in which the decision process terminates in finite time. Alternative concepts, such as episode-wise visitation frequency, are used in episodic RL algorithms, which are not only inconsistent with their counterparts in continual RL, and also make it harder to design and analyze RL algorithms in the episodic setting.