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A general criterion and an algorithmic framework for learning in multi-agent systems
- Machine Learning
, 2007
"... in multi-agent systems ..."
Hedged learning: regretminimization with learning experts
- In ICML ’05: Proceedings of the 22nd international conference on Machine learning
, 2005
"... In non-cooperative multi-agent situations, there cannot exist a globally optimal, yet opponent-independent learning algorithm. Regret-minimization over a set of strategies optimized for potential opponent models is proposed as a good framework for deciding how to behave in such situations. Using lon ..."
Abstract
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In non-cooperative multi-agent situations, there cannot exist a globally optimal, yet opponent-independent learning algorithm. Regret-minimization over a set of strategies optimized for potential opponent models is proposed as a good framework for deciding how to behave in such situations. Using longer playing horizons and experts that learn as they play, the regret-minimization framework can be extended to overcome several shortcomings of earlier approaches to the problem of multi-agent learning. 1.
