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» RoboCup: Today and Tomorrow - What we have learned
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ATAL
2006
Springer
15 years 1 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»
14 years 8 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
14 years 11 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
15 years 3 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»
15 years 3 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...