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ECML
2006
Springer
13 years 11 months ago
Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery
Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity. We present approaches that ...
Scott Proper, Prasad Tadepalli
APPROX
2009
Springer
138views Algorithms» more  APPROX 2009»
14 years 2 months ago
Submodular Maximization over Multiple Matroids via Generalized Exchange Properties
Submodular-function maximization is a central problem in combinatorial optimization, generalizing many important NP-hard problems including Max Cut in digraphs, graphs and hypergr...
Jon Lee, Maxim Sviridenko, Jan Vondrák
IPCO
2010
148views Optimization» more  IPCO 2010»
13 years 9 months ago
Prize-Collecting Steiner Network Problems
In the Steiner Network problem we are given a graph with edge-costs and connectivity requirements between node pairs , . The goal is to find a minimum-cost subgraph of that contain...
MohammadTaghi Hajiaghayi, Rohit Khandekar, Guy Kor...
ATAL
2010
Springer
13 years 8 months ago
Linear options
Learning, planning, and representing knowledge in large state t multiple levels of temporal abstraction are key, long-standing challenges for building flexible autonomous agents. ...
Jonathan Sorg, Satinder P. Singh
ML
2002
ACM
154views Machine Learning» more  ML 2002»
13 years 7 months ago
Technical Update: Least-Squares Temporal Difference Learning
TD() is a popular family of algorithms for approximate policy evaluation in large MDPs. TD() works by incrementally updating the value function after each observed transition. It h...
Justin A. Boyan