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» RoboCup: Today and Tomorrow - What we have learned
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ATAL
2006
Springer
13 years 11 months ago
Learning the required number of agents for complex tasks
Coordinating agents in a complex environment is a hard problem, but it can become even harder when certain characteristics of the tasks, like the required number of agents, are un...
Sébastien Paquet, Brahim Chaib-draa
RAS
2010
131views more  RAS 2010»
13 years 6 months ago
Probabilistic Policy Reuse for inter-task transfer learning
Policy Reuse is a reinforcement learning technique that efficiently learns a new policy by using past similar learned policies. The Policy Reuse learner improves its exploration b...
Fernando Fernández, Javier García, M...
AIIDE
2006
13 years 9 months ago
The Self Organization of Context for Learning in MultiAgent Games
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...
Christopher D. White, Dave Brogan
IEAAIE
2005
Springer
14 years 1 months ago
Movement Prediction from Real-World Images Using a Liquid State Machine
Prediction is an important task in robot motor control where it is used to gain feedback for a controller. With such a self-generated feedback, which is available before sensor rea...
Harald Burgsteiner, Mark Kröll, Alexander Leo...
ROBOCUP
2005
Springer
155views Robotics» more  ROBOCUP 2005»
14 years 1 months ago
An Application Interface for UCHILSIM and the Arrival of New Challenges
UCHILSIM is a robot simulator recently introduced in the RoboCup Four Legged League. A main attractive of the simulator is the possibility of reproducing with accuracy the dynamica...
Juan Cristóbal Zagal, Iván Sarmiento...