Convergence, Targeted Optimality, and Safety in Multiagent Learning
Abstract
This paper introduces a novel multiagent learning algorithm, Convergence with Model Learning and Safety (or CMLeS in short), which achieves convergence, targeted optimality against memory-bounded adversaries, and safety, in arbitrary repeated games. The most novel aspect of CMLeS is the manner in which it guarantees(in a PAC sense) targeted optimality against memory-bounded adversaries, via efficient exploration and exploitation. CMLeS is fully implemented and we present empirical results demonstrating its effectiveness.
Cite
Text
Chakraborty and Stone. "Convergence, Targeted Optimality, and Safety in Multiagent Learning." International Conference on Machine Learning, 2010. doi:10.1007/978-3-319-02606-0_4Markdown
[Chakraborty and Stone. "Convergence, Targeted Optimality, and Safety in Multiagent Learning." International Conference on Machine Learning, 2010.](https://mlanthology.org/icml/2010/chakraborty2010icml-convergence/) doi:10.1007/978-3-319-02606-0_4BibTeX
@inproceedings{chakraborty2010icml-convergence,
title = {{Convergence, Targeted Optimality, and Safety in Multiagent Learning}},
author = {Chakraborty, Doran and Stone, Peter},
booktitle = {International Conference on Machine Learning},
year = {2010},
pages = {191-198},
doi = {10.1007/978-3-319-02606-0_4},
url = {https://mlanthology.org/icml/2010/chakraborty2010icml-convergence/}
}