Integration of Reinforcement Learning and Deep Learning in Path Planning Games

Abstract

This paper explores the dynamic intersection of Reinforcement Learning (RL) and Deep Learning (DL), emphasizing their collaborative strength in the realm of Deep Reinforcement Learning (DRL). The fusion of RL's strategy optimization through agent-environment interactions and DL's advanced perception and decision-making abilities results in DRL's powerful framework. This framework is extensively utilized to tackle intricate decision-making challenges across diverse fields. The paper begins by laying a solid theoretical foundation, explaining the core principles of both reinforcement learning and deep learning, and highlighting their synergistic potential. Further, the focus shifts to the practical implementation of the Deep Q Network (DQN) algorithm, particularly in the context of elementary path analysis problems. This segment starts with an accessible introduction to deep neural networks, paving the way for a deeper understanding of DQN. It then explores key aspects such as the utilization and accumulation of experiences, a critical component of the learning process in DRL. Additionally, the paper addresses the inherent limitations of the DQN algorithm, suggesting avenues for potential improvements and enhancements.