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AAMAS
2007
Springer
14 years 3 months ago
Bifurcation Analysis of Reinforcement Learning Agents in the Selten's Horse Game
Abstract. The application of reinforcement learning algorithms to multiagent domains may cause complex non-convergent dynamics. The replicator dynamics, commonly used in evolutiona...
Alessandro Lazaric, Jose Enrique Munoz de Cote, Fa...
TSMC
2008
229views more  TSMC 2008»
13 years 8 months ago
A Comprehensive Survey of Multiagent Reinforcement Learning
Multiagent systems are rapidly finding applications in a variety of domains, including robotics, distributed control, telecommunications, and economics. The complexity of many task...
Lucian Busoniu, Robert Babuska, Bart De Schutter
ICRA
2010
IEEE
145views Robotics» more  ICRA 2010»
13 years 7 months ago
Reinforcement learning of motor skills in high dimensions: A path integral approach
— Reinforcement learning (RL) is one of the most general approaches to learning control. Its applicability to complex motor systems, however, has been largely impossible so far d...
Evangelos Theodorou, Jonas Buchli, Stefan Schaal
ICML
2006
IEEE
14 years 9 months ago
An analytic solution to discrete Bayesian reinforcement learning
Reinforcement learning (RL) was originally proposed as a framework to allow agents to learn in an online fashion as they interact with their environment. Existing RL algorithms co...
Pascal Poupart, Nikos A. Vlassis, Jesse Hoey, Kevi...
GECCO
2009
Springer
124views Optimization» more  GECCO 2009»
14 years 1 months ago
Reinforcement learning for games: failures and successes
We apply CMA-ES, an evolution strategy with covariance matrix adaptation, and TDL (Temporal Difference Learning) to reinforcement learning tasks. In both cases these algorithms se...
Wolfgang Konen, Thomas Bartz-Beielstein