Cobo et al., 2014 - Google Patents
Abstraction from demonstration for efficient reinforcement learning in high-dimensional domainsCobo et al., 2014
View HTML- Document ID
- 14215197176102428054
- Author
- Cobo L
- Subramanian K
- Isbell Jr C
- Lanterman A
- Thomaz A
- Publication year
- Publication venue
- Artificial Intelligence
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Snippet
Reinforcement learning (RL) and learning from demonstration (LfD) are two popular families of algorithms for learning policies for sequential decision problems, but they are often ineffective in high-dimensional domains unless provided with either a great deal of problem …
- 230000002787 reinforcement 0 title abstract description 46
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- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/04—Architectures, e.g. interconnection topology
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- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/08—Learning methods
- G06N3/082—Learning methods modifying the architecture, e.g. adding or deleting nodes or connections, pruning
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- G06—COMPUTING; CALCULATING; COUNTING
- G06N—COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N99/00—Subject matter not provided for in other groups of this subclass
- 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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- G06N3/00—Computer systems based on biological models
- G06N3/02—Computer systems based on biological models using neural network models
- G06N3/06—Physical realisation, i.e. hardware implementation of neural networks, neurons or parts of neurons
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- G06—COMPUTING; CALCULATING; COUNTING
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- G06N5/00—Computer systems utilising knowledge based models
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- G06N3/004—Artificial life, i.e. computers simulating life
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