Reinforcement Learning and Model-based Planning in Practice: A Survey of Algorithmic Rationale and Domain Applications

Reinforcement Learning (RL) is a foundational framework in Artificial Intelligence (AI) that enables agents to acquire optimal decision-making strategies through interactions with their environments. Building on principles of trial-and-error learning, RL adapts dynamically by leveraging feedback in the form of rewards or penalties. This paper provides a comprehensive survey of RL, its integration with Deep Learning into Deep Reinforcement Learning (DRL), and the emerging field of model-based planning. It systematically examines a wide range of algorithms, from classical tabular methods to state-of-the-art DRL techniques, while evaluating their scalability, sample efficiency, and applicability in various domains. The survey offers an in-depth analysis of the strengths and limitations of these approaches, along with practical guidance for selecting and implementing RL algorithms. Key challenges, such as convergence, stability, and the exploration-exploitation trade-off, are thoroughly addressed to tackle real-world constraints. Additionally, this work explores the transformative impact of RL across diverse fields, including robotics, autonomous systems, intelligent transportation, energy management, and financial modeling. By bridging theoretical advancements and practical applications, this survey aims to provide researchers and practitioners with a comprehensive understanding of RL’s capabilities for solving complex and dynamic problems in AI and beyond.