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AAMAS
2005
Springer
13 years 10 months ago
Cooperative Multi-Agent Learning: The State of the Art
Cooperative multi-agent systems are ones in which several agents attempt, through their interaction, to jointly solve tasks or to maximize utility. Due to the interactions among t...
Liviu Panait, Sean Luke
AI
1998
Springer
13 years 9 months ago
Model-Based Average Reward Reinforcement Learning
Reinforcement Learning (RL) is the study of programs that improve their performance by receiving rewards and punishments from the environment. Most RL methods optimize the discoun...
Prasad Tadepalli, DoKyeong Ok
GECCO
2004
Springer
155views Optimization» more  GECCO 2004»
14 years 3 months ago
Genetic Network Programming with Reinforcement Learning and Its Performance Evaluation
A new graph-based evolutionary algorithm named “Genetic Network Programming, GNP” has been proposed. GNP represents its solutions as directed graph structures, which can improv...
Shingo Mabu, Kotaro Hirasawa, Jinglu Hu
AAAI
2010
13 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
BCEC
1997
13 years 11 months ago
Adaptive Task Allocation Inspired by a Model of Division of Labor in Social Insects
Social insects provide us with a powerful metaphor to create decentralized systems of simple interacting, and often mobile, agents. The emergent collective intelligence of social i...
Eric Bonabeau, Andrej Sobkowski, Guy Theraulaz, Je...