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

Understand

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.