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IJCAI
2003
13 years 10 months ago
Multiple-Goal Reinforcement Learning with Modular Sarsa(0)
We present a new algorithm, GM-Sarsa(0), for finding approximate solutions to multiple-goal reinforcement learning problems that are modeled as composite Markov decision processe...
Nathan Sprague, Dana H. Ballard
NIPS
2008
13 years 10 months ago
MDPs with Non-Deterministic Policies
Markov Decision Processes (MDPs) have been extensively studied and used in the context of planning and decision-making, and many methods exist to find the optimal policy for probl...
Mahdi Milani Fard, Joelle Pineau
IROS
2007
IEEE
168views Robotics» more  IROS 2007»
14 years 3 months ago
Improving humanoid locomotive performance with learnt approximated dynamics via Gaussian processes for regression
Abstract— We propose to improve the locomotive performance of humanoid robots by using approximated biped stepping and walking dynamics with reinforcement learning (RL). Although...
Jun Morimoto, Christopher G. Atkeson, Gen Endo, Go...
MEMETIC
2010
311views more  MEMETIC 2010»
13 years 3 months ago
Iterated local search with Powell's method: a memetic algorithm for continuous global optimization
In combinatorial solution spaces Iterated Local Search (ILS) turns out to be exceptionally successful. The question arises: is ILS also capable of improving the optimization proces...
Oliver Kramer
WSC
1998
13 years 10 months ago
Use of Simulation in Optimization of Maintenance Policies
Selecting an optimum maintenance policy independent of other parameters of the production system does not always yield the overall optimum operating conditions. For instance, high...
Farhad Azadivar, J. Victor Shu