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ICRA
2005
IEEE
140views Robotics» more  ICRA 2005»
15 years 5 months ago
Fast Reinforcement Learning for Vision-guided Mobile Robots
— This paper presents a new reinforcement learning algorithm for accelerating acquisition of new skills by real mobile robots, without requiring simulation. It speeds up Q-learni...
Tomás Martínez-Marín, Tom Duc...
SAT
2007
Springer
107views Hardware» more  SAT 2007»
15 years 5 months ago
Combining Adaptive Noise and Look-Ahead in Local Search for SAT
Abstract. The adaptive noise mechanism was introduced in Novelty+ to automatically adapt noise settings during the search [4]. The local search algorithm G2 WSAT deterministically ...
Chu Min Li, Wanxia Wei, Harry Zhang
ICCBR
2009
Springer
15 years 6 months ago
Improving Reinforcement Learning by Using Case Based Heuristics
This work presents a new approach that allows the use of cases in a case base as heuristics to speed up Reinforcement Learning algorithms, combining Case Based Reasoning (CBR) and ...
Reinaldo A. C. Bianchi, Raquel Ros, Ramon Ló...
ESANN
2001
15 years 1 months ago
Motor control and movement optimization learned by combining auto-imitative and genetic algorithms
In sensorimotor behaviour often a great movement execution variability is combined with a relatively low error in reaching the intended goal. This phenomenon can especially be obse...
Karl-Theodor Kalveram, Ulrich Nakte
NIPS
2008
15 years 1 months ago
Optimization on a Budget: A Reinforcement Learning Approach
Many popular optimization algorithms, like the Levenberg-Marquardt algorithm (LMA), use heuristic-based "controllers" that modulate the behavior of the optimizer during ...
Paul Ruvolo, Ian R. Fasel, Javier R. Movellan