A Survey of Reinforcement Learning Algorithms for Dynamically Varying Environments (2005.10619v1)

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.

Reinforcement Learning in Dynamically Varying Environments: A Survey

Sindhu Padakandla's paper provides a comprehensive survey of reinforcement learning (RL) methods adapted to dynamically varying environments, which is a critical aspect of deploying RL in diverse real-world applications. Traditional RL algorithms primarily assume stationarity in the environment, meaning that the transition probabilities and reward functions do not change over time. This assumption restricts their applicability in complex domains such as inventory control, routing in communication networks, and intelligent transportation systems where environmental dynamics are often non-stationary.

Problem Statement

The paper identifies the prevalent assumptions in reinforcement learning related to stationary environments and the need to relax these assumptions when dealing with real-world temporal variations. It highlights the challenge in adapting single-agent RL techniques to situations where the environment model changes or evolves over time. The paper formulates this issue as learning efficient policies in a non-stationary Markov Decision Process (MDP) framework, where the functions dictating rewards and state transitions are not fixed but rather change at various intervals.

Solution Approaches

The survey categorizes existing solution approaches based on finite and infinite decision horizons. Finite horizon methods operate under the performance measure of regret minimization, which captures the notion of cumulative rewards missed by the RL agent during learning compared to the best hindsight policy. UCRL2 and its variation-aware counterpart are prominent examples of this category, employing confidence sets based on estimated transition probability and reward functions. They, however, hinge on restarts triggered by environmental changes, which can be a limitation in practical applications with frequent model shifts.

Infinite horizon approaches focus more on developing control policies that are adaptive to these shifts. Methods such as RLCD employ context detection to identify the active model in dynamically varying conditions, allowing agents to select decisions conducive to the current environment model. These approaches tend to leverage function approximation and change detection techniques to track and adapt to model changes efficiently.

Challenges and Implications

Several challenges are inherent in deploying RL in non-stationary environments: ensuring sample efficiency, managing the computational burden, and deriving theoretical convergence results remain crucial hurdles. The survey emphasizes the high memory and computation demands of algorithms, which limit scalability. Addressing these demands is essential for deploying RL in larger, complex systems. Theoretical research is necessary to provide convergence guarantees for model-free methods under non-stationary conditions.

Furthermore, the survey underscores the importance of continual learning and meta-learning in addressing the problem of dynamically varying environments. Both concepts enhance learning efficiency by leveraging past experience to inform future decisions, thereby reducing the adverse effects of catastrophic forgetting. Applying these learning paradigms can significantly benefit situations where patterns of environment dynamics are predictable.

Practical Applications and Future Research

The paper lays out the practical implications of RL in dynamically adaptive environments across different domains, including traffic management systems, robotics, and digital marketing. Each of these domains harbors unique environment dynamics necessitating tailored RL approaches.

Looking forward, the development of non-stationary RL algorithms holds promise in fields requiring real-time decision-making amidst volatile conditions. Future research must focus on combining robust change detection methods with efficient learning frameworks, enabling RL to adapt smoothly to continual changes.

In conclusion, Sindhu Padakandla's survey provides critical insights into the field of RL in non-stationary environments, illustrating the need for evolving classical RL methods to meet real-world complexity and variability. Adapting RL algorithms to dynamic conditions not only broadens their applicability but also strengthens the robustness and adaptability of AI systems in complex environments.