Sciweavers

7 search results - page 1 / 2
» Reinforcement Learning in Continuous Action Spaces through S...
Sort
View
NIPS
2007
13 years 5 months ago
Reinforcement Learning in Continuous Action Spaces through Sequential Monte Carlo Methods
Learning in real-world domains often requires to deal with continuous state and action spaces. Although many solutions have been proposed to apply Reinforcement Learning algorithm...
Alessandro Lazaric, Marcello Restelli, Andrea Bona...
ICML
2009
IEEE
14 years 4 months ago
Binary action search for learning continuous-action control policies
Reinforcement Learning methods for controlling stochastic processes typically assume a small and discrete action space. While continuous action spaces are quite common in real-wor...
Jason Pazis, Michail G. Lagoudakis
CDC
2009
IEEE
173views Control Systems» more  CDC 2009»
13 years 8 months ago
Sequentially updated Probability Collectives
— Multi-agent coordination problems can be cast as distributed optimization tasks. Probability Collectives (PCs) are techniques that deal with such problems in discrete and conti...
Michalis Smyrnakis, David S. Leslie
ICML
2004
IEEE
14 years 4 months ago
Learning to fly by combining reinforcement learning with behavioural cloning
Reinforcement learning deals with learning optimal or near optimal policies while interacting with the environment. Application domains with many continuous variables are difficul...
Eduardo F. Morales, Claude Sammut
JMLR
2010
163views more  JMLR 2010»
12 years 10 months ago
Active Sequential Learning with Tactile Feedback
We consider the problem of tactile discrimination, with the goal of estimating an underlying state parameter in a sequential setting. If the data is continuous and highdimensional...
Hannes Saal, Jo-Anne Ting, Sethu Vijayakumar