Adaptive Exploration Strategies for Reinforcement Learning
碩士 === 國立中山大學 === 電機工程學系研究所 === 104 === Reinforcement learning through an agent to learn policy use trial and error method to achieve the goal, but when we want to apply it in a real environment, how to dividing state space becomes difficult to decide, another problem in reinforcement learning, agen...
| Main Authors: | , |
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2016
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| Online Access: | http://ndltd.ncl.edu.tw/handle/77497896626363114047 |