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» Combining Learned Discrete and Continuous Action Models
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AAAI
2011
7 years 11 months ago
Combining Learned Discrete and Continuous Action Models
Action modeling is an important skill for agents that must perform tasks in novel domains. Previous work on action modeling has focused on learning STRIPS operators in discrete, r...
Joseph Z. Xu, John E. Laird
ATAL
2007
Springer
9 years 3 months ago
On discovery and learning of models with predictive representations of state for agents with continuous actions and observations
Models of agent-environment interaction that use predictive state representations (PSRs) have mainly focused on the case of discrete observations and actions. The theory of discre...
David Wingate, Satinder P. Singh
FLAIRS
2008
9 years 1 months ago
Learning Continuous Action Models in a Real-Time Strategy Environment
Although several researchers have integrated methods for reinforcement learning (RL) with case-based reasoning (CBR) to model continuous action spaces, existing integrations typic...
Matthew Molineaux, David W. Aha, Philip Moore
ICML
2009
IEEE
9 years 12 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
TSMC
2002
102views more  TSMC 2002»
8 years 10 months ago
Generalized pursuit learning schemes: new families of continuous and discretized learning automata
The fastest learning automata (LA) algorithms currently available fall in the family of estimator algorithms introduced by Thathachar and Sastry [24]. The pioneering work of these ...
M. Agache, B. John Oommen
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