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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Altahhan, A. |
| Copyright Year | 2014 |
| Description | Author affiliation: Coll. of Comput. Inf. Technol., American Univ. in the Emirates, United Arab Emirates (Altahhan, A.) |
| Abstract | This work describes a fast learning robot goal-aware navigation model that employs both gradient and conjugate gradient Temporal Difference (TD, TD-conj) methods. It builds on the fact that TD-conj was proven to be equivalent to a gradient TD method with a variable lambda under certain conditions. Based on straightforward features extraction process combined with goal-aware capabilities provided by whole image measure, the model solves what we call u-turn-homing benchmark problem without using landmarks. Only one goal snapshot was used with agent facing the goal directly. Therefore a novel threshold stopping formula was used to recognize the goal which is less sensitive to the agent-goal orientation problem. Unlike other models, this model refrains from artificially manipulating or assuming a priori knowledge about the environment, two constraints that widely restrict the applicability of existing models in realistic scenarios. An on-line control method was used to train a set of neural networks. With the aid of variable and fixed eligibility traces, these networks approximate the agent's action-value function allowing it to take close to optimal actions to reach its home. The effectiveness of the model was experimentally verified on an agent. |
| Starting Page | 1534 |
| Ending Page | 1541 |
| File Size | 3255698 |
| Page Count | 8 |
| File Format | |
| ISSN | 21614407 |
| e-ISBN | 9781479914845 |
| DOI | 10.1109/IJCNN.2014.6889845 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2014-07-06 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Robots Histograms Navigation Approximation algorithms Vectors Function approximation Orientation Insensitive Thersholding TD-conj Home Aware Variable 1 TD U-Turn-Homin |
| Content Type | Text |
| Resource Type | Article |
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