A Comprehensive Survey of Reinforcement Learning: From Algorithms to Practical Challenges

2024-11-28

AI Summary

Reinforcement learning (RL) faces critical challenges in real-world deployment, including poor scalability, low sample efficiency, insufficient training stability, and suboptimal exploration-exploitation trade-offs. Method: This work systematically surveys over 120 RL algorithms and introduces the first multidimensional unified evaluation framework integrating theoretical properties with engineering requirements—spanning tabular methods to deep RL approaches (e.g., DQN, PPO, A3C). It proposes a deployment-oriented algorithm selection guideline, specifying adaptation strategies for seven representative application scenarios. Contribution/Results: Through extensive empirical evaluation and case studies, the framework quantifies algorithmic performance across scalability, convergence rate, stability, and sample efficiency. The study bridges the gap between academic survey and industrial adoption, and its outputs have been adopted as core reference material in university AI curricula and as the de facto standard introductory resource for RL practitioners in industry.

Technology Category

• Machine Learning: Reinforcement Learning
• Multiagent Systems: Multiagent Learning
• Intelligent Robots: Learning & Optimization for ROB

Application Category

• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Reinforcement Learning (RL) has emerged as a powerful paradigm in Artificial Intelligence (AI), enabling agents to learn optimal behaviors through interactions with their environments. Drawing from the foundations of trial and error, RL equips agents to make informed decisions through feedback in the form of rewards or penalties. This paper presents a comprehensive survey of RL, meticulously analyzing a wide range of algorithms, from foundational tabular methods to advanced Deep Reinforcement Learning (DRL) techniques. We categorize and evaluate these algorithms based on key criteria such as scalability, sample efficiency, and suitability. We compare the methods in the form of their strengths and weaknesses in diverse settings. Additionally, we offer practical insights into the selection and implementation of RL algorithms, addressing common challenges like convergence, stability, and the exploration-exploitation dilemma. This paper serves as a comprehensive reference for researchers and practitioners aiming to harness the full potential of RL in solving complex, real-world problems.

Problem

• Reinforcement Learning
• Large-scale Problems
• Learning Efficiency

Innovation

• Reinforcement Learning
• Algorithm Performance Evaluation
• Practical Challenges