Dynamic Path Planning of Unknown Environment Based on Deep Reinforcement Learning
Dynamic path planning of unknown environment has always been a challenge for mobile robots. In this paper, we apply double Q-network (DDQN) deep reinforcement learning proposed by DeepMind in 2016 to dynamic path planning of unknown environment. The reward and punishment function and the training me...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2018-01-01
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| Series: | Journal of Robotics |
| Online Access: | http://dx.doi.org/10.1155/2018/5781591 |
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