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ISCIS
2009
Springer
15 years 4 months ago
Predicting future object states using learned affordances
Abstract—The notion of affordances was proposed by J.J. Gibson, to refer to the action possibilities offered to the organism by its environment. In a previous formalization, affo...
Emre Ugur, Erol Sahin, Erhan Oztop
ICANNGA
2007
Springer
105views Algorithms» more  ICANNGA 2007»
15 years 4 months ago
Reinforcement Learning in Fine Time Discretization
Reinforcement Learning (RL) is analyzed here as a tool for control system optimization. State and action spaces are assumed to be continuous. Time is assumed to be discrete, yet th...
Pawel Wawrzynski
92
Voted
COLT
2003
Springer
15 years 3 months ago
On-Line Learning with Imperfect Monitoring
We study on-line play of repeated matrix games in which the observations of past actions of the other player and the obtained reward are partial and stochastic. We define the Part...
Shie Mannor, Nahum Shimkin
ICRA
1995
IEEE
79views Robotics» more  ICRA 1995»
15 years 1 months ago
Learning to predict Resistive Forces During Robotic Excavation
— Few robot tasks require as forceful an interaction with the world as excavation. In order to effectively plan its actions, our robot excavator requires a method that allows it ...
Sanjiv Singh
EWCBR
2008
Springer
14 years 12 months ago
Recognizing the Enemy: Combining Reinforcement Learning with Strategy Selection Using Case-Based Reasoning
This paper presents CBRetaliate, an agent that combines Case-Based Reasoning (CBR) and Reinforcement Learning (RL) algorithms. Unlike most previous work where RL is used to improve...
Bryan Auslander, Stephen Lee-Urban, Chad Hogg, H&e...