A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments

TL;DR

This survey reviews reinforcement learning algorithms designed for environments that change over time, emphasizing methods that enable autonomous agents to adapt efficiently without assuming stationarity.

Contribution

It provides a comprehensive categorization and analysis of RL algorithms tailored for non-stationary environments, highlighting their strengths and limitations.

Findings

Various RL algorithms effectively adapt to changing environments

Categorization of algorithms based on their adaptation strategies

Discussion of future research directions in non-stationary RL

Abstract

Reinforcement learning (RL) algorithms find applications in inventory control, recommender systems, vehicular traffic management, cloud computing and robotics. The real-world complications of many tasks arising in these domains makes them difficult to solve with the basic assumptions underlying classical RL algorithms. RL agents in these applications often need to react and adapt to changing operating conditions. A significant part of research on single-agent RL techniques focuses on developing algorithms when the underlying assumption of stationary environment model is relaxed. This paper provides a survey of RL methods developed for handling dynamically varying environment models. The goal of methods not limited by the stationarity assumption is to help autonomous agents adapt to varying operating conditions. This is possible either by minimizing the rewards lost during learning by RL agent or by finding a suitable policy for the RL agent which leads to efficient operation of the underlying system. A representative collection of these algorithms is discussed in detail in this work along with their categorization and their relative merits and demerits. Additionally we also review works which are tailored to application domains. Finally, we discuss future enhancements for this field.