CLEAN Learning to Improve Coordination and Scalability in Multiagent Systems

Explore this paper's citation graph

Summary

This work introduces Coordinated Learning without Exploratory Action Noise (CLEAN) rewards which improve coordination and performance by utilizing the concept of private exploration in order to remove the negative impact of traditional “public” exploration strategies from learning in multiagent systems.

Type
dissertation
Published
2013-04-15
Cited by
3
References
151

Keywords

Scalability, Multi-agent system, Computer science, Distributed computing, Human–computer interaction

References

Cited by

Related papers