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ATAL
2008
Springer
13 years 9 months ago
Learning to interact: connecting perception with action in virtual environments
Modeling synthetic characters which interact with objects in dynamic virtual worlds is important when we want the agents to act in an autonomous and non-preplanned way. Such inter...
Pedro Sequeira, Ana Paiva
UAI
1996
13 years 8 months ago
Bayesian Learning of Loglinear Models for Neural Connectivity
This paper presents a Bayesian approach to learning the connectivity structure of a group of neurons from data on configuration frequencies. A major objective of the research is t...
Kathryn B. Laskey, Laura Martignon
ICCBR
2007
Springer
14 years 1 months ago
An Analysis of Case-Based Value Function Approximation by Approximating State Transition Graphs
We identify two fundamental points of utilizing CBR for an adaptive agent that tries to learn on the basis of trial and error without a model of its environment. The first link co...
Thomas Gabel, Martin Riedmiller
ICML
2007
IEEE
14 years 8 months ago
Constructing basis functions from directed graphs for value function approximation
Basis functions derived from an undirected graph connecting nearby samples from a Markov decision process (MDP) have proven useful for approximating value functions. The success o...
Jeffrey Johns, Sridhar Mahadevan
CORR
2010
Springer
204views Education» more  CORR 2010»
13 years 6 months ago
Predictive State Temporal Difference Learning
We propose a new approach to value function approximation which combines linear temporal difference reinforcement learning with subspace identification. In practical applications...
Byron Boots, Geoffrey J. Gordon