Cruz et al., 2017 - Google Patents
Path planning of multi-agent systems in unknown environment with neural kernel smoothing and reinforcement learningCruz et al., 2017
- Document ID
- 1095973740907034885
- Author
- Cruz D
- Yu W
- Publication year
- Publication venue
- Neurocomputing
External Links
Snippet
Path planning is a basic task of robot navigation, especially for autonomous robots. It is more complex and difficult for multi-agent systems. The popular reinforcement learning method cannot solve the path planning problem directly in unknown environment. In this paper, the …
- 230000001537 neural 0 title abstract description 36
Classifications
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- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06N3/04—Architectures, e.g. interconnection topology
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- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
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- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
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- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
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- G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
- G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
- G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
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- G06N99/005—Learning machines, i.e. computer in which a programme is changed according to experience gained by the machine itself during a complete run
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- G—PHYSICS
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- G06N3/00—Computer systems based on biological models
- G06N3/004—Artificial life, i.e. computers simulating life
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- G—PHYSICS
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