|
|
|
| Cloud Reasoning Model-Based Exploration for Deep Reinforcement Learning |
| LI Chenxi① CAO Lei① CHEN Xiliang① ZHANG Yongliang① XU Zhixiong① PENG Hui① DUAN Liwen② |
①(Institute of Command Information System, PLA University of Science and Technology, Nanjing 210007, China)
②(College of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China) |
|
|
|
|
Abstract Reinforcement learning which has self-improving and online learning properties gets the policy of tasks through the interaction with environment. But the mechanism of “trial-and-error” usually leads to a large number of training episodes. Knowledge includes human experience and the cognition of environment. This paper tries to introduce the qualitative rules into the reinforcement learning, and represents these rules through the cloud reasoning model. It is used as the heuristics exploration strategy to guide the action selection. Empirical evaluation is conducted in OpenAI Gym environment called “CartPole-v2” and the result shows that using exploration strategy based on the cloud reasoning model significantly enhances the performance of the learning process.
|
|
Received: 18 April 2017
Published: 01 November 2017
|
|
|
| Fund:The Advanced Research of China Electronics Technology Group Corporation (6141B08010101), China Postdoctoral Science Foundation (2015T81081, 2016M602974), The Jiangsu Natural Science Foundation for Youths (BK20140075) |
|
Corresponding Authors:
LI Chenxi
E-mail: streamorning@qq.com
|
|
|
|
| [1] |
SUTTON R S and BARTO A G. Reinforcement Learning: An Introduction[M]. MA: MIT Press, 1998: 3-24. doi: 10.1109/ TNN.1998.712192.
|
| [2] |
MNIH V, KAVUKCUOGLU K, SILVER D, et al. Playing Atari with deep reinforcement learning[OL]. https://arxiv.org /abs/1312.5602v1, 2013.12.
|
| [3] |
MNIH V, KAVUKCUOGLU K, SILVER D, et al. Human- level control through deep reinforcement learning[J]. Nature, 2015, 518(7540): 529-533. doi: 10.1038/nature14236.
|
| [4] |
OSBAND I, BLUNDELL C, PRITZEL A, et al. Deep exploration via bootstrapped DQN[C]. Proceedings of the 29th Neural Information Processing Systems, Barcelona, 2016: 4026-4034.
|
| [5] |
BELLEMARE M, SRINIVASAN S, OSTROVSKI G, et al. Unifying count-based exploration and intrinsic motivation[C]. Proceedings of the 29th Neural Information Processing Systems, Barcelona, 2016: 1471-1479.
|
| [6] |
HOUTHOOFT R, CHEN X, DUAN Y, et al. VIME: Variational information maximizing exploration[C]. Proceedings of the 29th Neural Information Processing Systems, Barcelona, 2016: 1109-1117.
|
| [7] |
DAVENPORT T H, PRUSAK L, and PRUSAK L. Working Knowledge: How Organizations Manage What They Know [M]. Boston: Harvard Business School Press, 1997: 1-24. doi: 10.1145/347634.348775.
|
| [8] |
SANTOS M and BOTELLA G. Dyna-H: A heuristic planning reinforcement learning algorithm applied to role-playing game strategy decision systems[J]. Knowledge-Based Systems, 2012, 32(8): 28-36.
|
| [9] |
BIANCHI R A C, ROS R, and MANTARAS R L D. Improving reinforcement learning by using case based heuristics[C]. Proceedings of the International Conference on Case-Based Reasoning: Case-Based Reasoning Research and Development, Burlin, 2009: 75-89.
|
| [10] |
KUHLMANN G, STONE P, MOONEY R, et al. Guiding a reinforcement learner with natural language advice: Initial results in RoboCup soccer[C]. Proceedings of the 19th National Conference on Artificial Intelligence Workshop on Supervisory Control of Learning and Adaptive Systems, California, 2004: 30-35.
|
| [11] |
LI Deyi, CHEUNG D, SHI Xuemei, et al. Uncertainty reasoning based on cloud models in controllers[J]. Computers & Mathematics with Applications, 1998, 35(3): 99-123.
|
| [12] |
SINGH S P. Learning to solve Markovian decision processes [D]. [Ph.D. dissertation], University of Massachusetts, Amherst, 1994: 66-72.
|
| [13] |
HASSELT H V, GUEZ A, and SILVER D. Deep reinforcement learning with double Q-learning[C]. Proceedings of the 30th AAAI Conference on Articial Intelligence, Phoenix, 2016: 2094-2100.
|
|
|
|
