Earticle

초록

영어

Reinforcement learning can be applied to a wide variety of problems. However, the fundamental limitation of reinforcement learning is that it is difficult to derive an answer within a given time because the problems in the real world are too complex. Then, with the development of neural network technology, research on deep reinforcement learning that combines deep learning with reinforcement learning is receiving lots of attention. In this paper, two types of neural networks are combined with reinforcement learning and their characteristics were compared and analyzed with existing value-based reinforcement learning algorithms. Two types of neural networks are FNN and CNN, and existing reinforcement learning algorithms are SARSA and Qlearning.

목차

Abstract
1. INTRODUCTION
2. Cart-pole system
3. Value-Based Reinforcement Learning
3.1. Traditional reinforcement learning
3.2. Reinforcement learning with Neural Network
4. EMPIRICAL RESULTS AND OBSERVATION
4.1. RL Reward of cart-pole system
4.2. Pole Oscillation Angle of cart-pole system
5. CONCLUSION
ACKNOWLEDGEMENT
REFERENCES

키워드

저자

• Byeong-Chan Han [ Graduate student, Dept. of Electronic Engineering, Jeju National University, Korea ]
• Ho-Chan Kim [ Professor, Dept. of Electrical Engineering, Jeju National University, Korea ]
• Min-Jae Kang [ Professor, Dept of Electronic Engineering, Jeju National University, Korea ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Internet, Broadcasting and Communication

• 간기

  계간

• pISSN

  2288-4920

• eISSN

  2288-4939

• 수록기간

  2009~2025

• 십진분류

  KDC 326 DDC 380