Deep Reinforcement Learning with Sarsa and Q-Learning: A Hybrid Approach-Reference-Cited by-同舟云学术

Deep Reinforcement Learning with Sarsa and Q-Learning: A Hybrid Approach

Published:2018-09-01 Issue:9 Volume:E101.D Page:2315-2322
ISSN:0916-8532
Container-title:IEICE Transactions on Information and Systems
language:en
Short-container-title:IEICE Trans. Inf. & Syst.

Author:

XU Zhi-xiong¹,CAO Lei¹,CHEN Xi-liang¹,LI Chen-xi¹,ZHANG Yong-liang¹,LAI Jun¹

Affiliation:

1. Institute of Command Information System, PLA University of Science and Technology

Publisher

Institute of Electronics, Information and Communications Engineers (IEICE)

Subject

Artificial Intelligence,Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Hardware and Architecture,Software

Link

https://www.jstage.jst.go.jp/article/transinf/E101.D/9/E101.D_2017EDP7278/_pdf

Reference28 articles.

1. [1] R.S. Sutton and A.G. Barto, Introduction to Reinforcement Learning, Decision Theory Models for Applications in Artificial Intelligence: Concepts and Solutions, pp.90-127, 2011.

2. [2] C.H.C.J. Watkins, “Learning from delayed rewards,” Robotics & Autonomous Systems, vol.15, no.4, pp.233-235, 1989.

3. [3] S. Thrun and A. Schwartz, “Issues in using function approximation for reinforcement learning,” Proc. Fourth Connectionist Models Summer School, vol.14, no.3, pp.65-90, 1993.

4. [4] H.V. Hasselt, “Double Q-learning,” Advances in Neural Information Processing Systems 23, Proceedings of A Meeting Held 6-9 Dec. 2010, Conference on Neural Information Processing Systems 2010, Vancouver, British Columbia, Canada, OAI, pp.2613-2621, 2010.

5. [5] V. Mnih, K. Kavukcuoglu, D. Silver, et al., “Playing Atari with deep reinforcement learning,” arXiv preprint arXiv:1312.5602v1 [cs.LG], 2013.

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