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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Parker, M. Bryant, B.D. |
| Copyright Year | 2009 |
| Description | Author affiliation: Department of Computer Science and Engineering, University of Nevada, Reno, USA (Parker, M.; Bryant, B.D.) |
| Abstract | Backpropagation and neuroevolution are used in a Lamarckian evolution process to train a neural network visual controller for agents in the Quake II environment. In previous work, we hand-coded a non-visual controller for supervising in backpropagation, but hand-coding can only be done for problems with known solutions. In this research the problem for the agent is to attack a moving enemy in a visually complex room with a large central pillar. Because we did not know a solution to the problem, we could not hand-code a supervising controller; instead, we evolve a non-visual neural network as supervisor to the visual controller. This setup creates controllers that learn much faster and have a greater fitness than those learning by neuroevolution-only on the same problem in the same amount of time. |
| Starting Page | 287 |
| Ending Page | 293 |
| File Size | 443362 |
| Page Count | 7 |
| File Format | |
| ISBN | 9781424448142 |
| DOI | 10.1109/CIG.2009.5286462 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-09-07 |
| Publisher Place | Italy |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Backpropagation Computer science Navigation Computational modeling Robot vision systems Neural networks Humans Gray-scale Cameras Biological neural networks |
| Content Type | Text |
| Resource Type | Article |
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