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» Iterative Learning Control - Monotonicity and Optimization
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UAI
2008
14 years 11 months ago
CORL: A Continuous-state Offset-dynamics Reinforcement Learner
Continuous state spaces and stochastic, switching dynamics characterize a number of rich, realworld domains, such as robot navigation across varying terrain. We describe a reinfor...
Emma Brunskill, Bethany R. Leffler, Lihong Li, Mic...
CDC
2010
IEEE
117views Control Systems» more  CDC 2010»
14 years 5 months ago
A symmetric structure of variational and adjoint systems of stochastic Hamiltonian systems
Abstract-- The authors have extended deterministic portHamiltonian systems into stochastic dynamical systems which are described by stochastic differential equations written in the...
Satoshi Satoh, Kenji Fujimoto
ATAL
2009
Springer
15 years 4 months ago
Generalized model learning for reinforcement learning in factored domains
Improving the sample efficiency of reinforcement learning algorithms to scale up to larger and more realistic domains is a current research challenge in machine learning. Model-ba...
Todd Hester, Peter Stone
ECAI
2010
Springer
14 years 11 months ago
Bayesian Monte Carlo for the Global Optimization of Expensive Functions
In the last decades enormous advances have been made possible for modelling complex (physical) systems by mathematical equations and computer algorithms. To deal with very long run...
Perry Groot, Adriana Birlutiu, Tom Heskes
ECML
2006
Springer
15 years 1 months ago
Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery
Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity. We present approaches that ...
Scott Proper, Prasad Tadepalli