Exploration in Deep Reinforcement Learning: A Survey

Exploration is a critical component in Deep Reinforcement Learning (DRL), directly impacting how efficiently agents can learn optimal policies. This paper surveys different exploration strategies, ranging from simple heuristic methods to advanced, theoretically driven techniques. We focus on their application in various DRL environments, discussing their strengths, weaknesses, and adaptability. The goal of this paper is to provide a thorough examination of exploration methods and highlight their importance in DRL systems, presenting experimental results that compare these strategies under different conditions. The results demonstrate that exploration techniques can significantly impact learning efficiency, and we propose a hybrid approach to optimize exploration in complex environments.