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AAMAS
2005
Springer

Advice-Exchange Between Evolutionary Algorithms and Reinforcement Learning Agents: Experiments in the Pursuit Domain

10 years 1 months ago
Advice-Exchange Between Evolutionary Algorithms and Reinforcement Learning Agents: Experiments in the Pursuit Domain
This research aims at studying the effects of exchanging information during the learning process in Multiagent Systems. The concept of advice-exchange, introduced in (Nunes and Oliveira, 2002b), consists in enabling an agent to request extra feedback, in the form of episodic advice, from other agents that are solving similar problems. The work that was previously focused on the exchange of information between agents that were solving detached problems is now concerned with groups of learning-agents that share the same environment. This change added new difficulties to the task. The experiments reported below were conducted to detect the causes and correct the shortcomings that emerged when moving from environments where agents worked in detached problems to those where agents are interacting in the same environment. New concepts, such as self confidence, trust and advisor preference are introduced in this text.
Luís Nunes, Eugénio C. Oliveira
Added 26 Jun 2010
Updated 26 Jun 2010
Type Conference
Year 2005
Where AAMAS
Authors Luís Nunes, Eugénio C. Oliveira
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