Earticle

초록

영어

This paper studies ε-greedy algorithm and softmax algorithm in obstacle avoidance and balance study. In the experiment, Sarsa algorithm and Q-Learning algorithm were used to appropriately simplify and build the model of obstacle avoidance; softmax algorithm was used to address how to balance exploration and utilisation; and two classical algorithms of reinforcement learning were adopted to deal with obstacle avoidance. The results generated by simulation prove that Sarsa algorithm and Q-Learning algorithm can handle obstacle avoidance and balance study in limited time step, which makes the intelligent agent improve the non-maximum estimated value of the value function of the state so as to choose the best action that has been carried out. In addition, Sarsa algorithm and Q-Learning algorithm can also enable the intelligent agent to try new actions and find out the optimal one.

목차

Abstract
 1. Introduction
 2. Reinforcement Learning
  2.1. Theory Framework of Reinforcement Learning
  2.2. Key Elements of Reinforcement Learning
  2.3. Exploration and Utilization
  2.4. Sarsa Algorithm
  2.5. Q-Learning Algorithm
 3. Obstacle Avoidance Model
 4. The Results of Simulation and its Analysis
 5. Conclusions
 Acknowledgments
 References

키워드

저자

• WANG Qi-ming [ College of Information Engineering, Pingdingshan University, Pingdingshan Henan 467002, China ]
• Liu Jian-fen [ College of Information Engineering, Pingdingshan University, Pingdingshan Henan 467002, China ]
• Shi He-sheng [ College of Information Engineering, Pingdingshan University, Pingdingshan Henan 467002, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Grid and Distributed Computing

• 간기

  격월간

• pISSN

  2005-4262

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605