Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function
Abstract
Learning how to adjust to an opponent's position is critical to the success of having intelligent agents collaborating towards the achievement of specific tasks in unfriendly environments. This pa(cid:173) per describes our work on a Memory-based technique for to choose an action based on a continuous-valued state attribute indicating the position of an opponent. We investigate the question of how an agent performs in nondeterministic variations of the training situ(cid:173) ations. Our experiments indicate that when the random variations fall within some bound of the initial training, the agent performs better with some initial training rather than from a tabula-rasa.
Cite
Text
Stone and Veloso. "Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function." Neural Information Processing Systems, 1995.Markdown
[Stone and Veloso. "Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function." Neural Information Processing Systems, 1995.](https://mlanthology.org/neurips/1995/stone1995neurips-beating/)BibTeX
@inproceedings{stone1995neurips-beating,
title = {{Beating a Defender in Robotic Soccer: Memory-Based Learning of a Continuous Function}},
author = {Stone, Peter and Veloso, Manuela M.},
booktitle = {Neural Information Processing Systems},
year = {1995},
pages = {896-902},
url = {https://mlanthology.org/neurips/1995/stone1995neurips-beating/}
}