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AAAI
2010
14 years 11 months ago
Multi-Agent Learning with Policy Prediction
Due to the non-stationary environment, learning in multi-agent systems is a challenging problem. This paper first introduces a new gradient-based learning algorithm, augmenting th...
Chongjie Zhang, Victor R. Lesser
ECAI
2010
Springer
14 years 11 months ago
The Dynamics of Multi-Agent Reinforcement Learning
Abstract. Infinite-horizon multi-agent control processes with nondeterminism and partial state knowledge have particularly interesting properties with respect to adaptive control, ...
Luke Dickens, Krysia Broda, Alessandra Russo
NIPS
2004
14 years 11 months ago
Convergence and No-Regret in Multiagent Learning
Learning in a multiagent system is a challenging problem due to two key factors. First, if other agents are simultaneously learning then the environment is no longer stationary, t...
Michael H. Bowling
JACM
2006
93views more  JACM 2006»
14 years 9 months ago
Combining expert advice in reactive environments
"Experts algorithms" constitute a methodology for choosing actions repeatedly, when the rewards depend both on the choice of action and on the unknown current state of t...
Daniela Pucci de Farias, Nimrod Megiddo
STOC
2007
ACM
111views Algorithms» more  STOC 2007»
15 years 10 months ago
Low-end uniform hardness vs. randomness tradeoffs for AM
In 1998, Impagliazzo and Wigderson [IW98] proved a hardness vs. randomness tradeoff for BPP in the uniform setting, which was subsequently extended to give optimal tradeoffs for t...
Ronen Shaltiel, Christopher Umans