Name
Abstract | ||
|---|---|---|
Multi-agent learning is a growing area of research. An important topic is to formulate how an agent can learn a good policy in the face of adaptive, competitive opponents. Most research has focused on extensions of single agent learning techniques originally designed for agents in more static environments. These techniques however fail to incorporate a notion of the effect of own previous actions on the development of the policy of the other agents in the system. We argue that incorporation of this property is beneficial in competitive settings. In this paper, we present a novel algorithm to capture this notion, and present experimental results to validate our claims. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1160633.1160763 | Journal of the American Society for Mass Spectrometry |
Keywords | Field | DocType |
induced change,good policy,static environment,present experimental result,competitive opponent,multi-agent learning,multi-agent game,novel algorithm,single agent,important topic,own previous action,competitive setting | Computer science,Artificial intelligence,Adversary,Error-driven learning,Machine learning | Conference |
ISBN | Citations | PageRank |
1-59593-303-4 | 1 | 0.38 |
References | Authors | |
14 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. J. 't Hoen | 1 | 8 | 1.27 |
| S. M. Bohte | 2 | 120 | 10.02 |
| J. A. La Poutré | 3 | 188 | 18.77 |