Sciweavers

Share
NIPS
2003

All learning is Local: Multi-agent Learning in Global Reward Games

10 years 4 months ago
All learning is Local: Multi-agent Learning in Global Reward Games
In large multiagent games, partial observability, coordination, and credit assignment persistently plague attempts to design good learning algorithms. We provide a simple and efficient algorithm that in part uses a linear system to model the world from a single agent’s limited perspective, and takes advantage of Kalman filtering to allow an agent to construct a good training signal and learn an effective policy.
Yu-Han Chang, Tracey Ho, Leslie Pack Kaelbling
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2003
Where NIPS
Authors Yu-Han Chang, Tracey Ho, Leslie Pack Kaelbling
Comments (0)
books