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ILP
2007
Springer
15 years 7 months ago
Building Relational World Models for Reinforcement Learning
Abstract. Many reinforcement learning domains are highly relational. While traditional temporal-difference methods can be applied to these domains, they are limited in their capaci...
Trevor Walker, Lisa Torrey, Jude W. Shavlik, Richa...
GLOBECOM
2006
IEEE
15 years 6 months ago
Optimal Routing Between Alternate Paths With Different Network Transit Delays
— We consider the path-determination problem in Internet core routers that distribute flows across alternate paths leading to the same destination. We assume that the remainder ...
Essia Hamouda Elhafsi, Mart Molle
FLAIRS
2008
15 years 3 months ago
State Space Compression with Predictive Representations
Current studies have demonstrated that the representational power of predictive state representations (PSRs) is at least equal to the one of partially observable Markov decision p...
Abdeslam Boularias, Masoumeh T. Izadi, Brahim Chai...
AAAI
2010
15 years 2 months ago
Using Bisimulation for Policy Transfer in MDPs
Knowledge transfer has been suggested as a useful approach for solving large Markov Decision Processes. The main idea is to compute a decision-making policy in one environment and...
Pablo Samuel Castro, Doina Precup
106
Voted
JCP
2008
139views more  JCP 2008»
15 years 24 days ago
Agent Learning in Relational Domains based on Logical MDPs with Negation
In this paper, we propose a model named Logical Markov Decision Processes with Negation for Relational Reinforcement Learning for applying Reinforcement Learning algorithms on the ...
Song Zhiwei, Chen Xiaoping, Cong Shuang