An Improved Deep Reinforcement Learning with Sparse Rewards
碩士 === 國立中山大學 === 電機工程學系研究所 === 107 === In reinforcement learning, how an agent explores in an environment with sparse rewards is a long-standing problem. An improved deep reinforcement learning described in this thesis encourages an agent to explore unvisited environmental states in an environment...
| Main Authors: | , |
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| Format: | Others |
| Language: | zh-TW |
| Published: |
2018
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| Online Access: | http://ndltd.ncl.edu.tw/handle/eq94pr |