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ATAL
2007
Springer
14 years 2 months ago
Model-based function approximation in reinforcement learning
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
Nicholas K. Jong, Peter Stone
AIPS
2008
13 years 11 months ago
Fast Dynamic Scheduling of Disjunctive Temporal Constraint Networks through Incremental Compilation
Autonomous systems operating in real-world environments must plan, schedule, and execute missions while robustly adapting to uncertainty and disturbance. One way to mitigate the e...
Julie A. Shah, Brian C. Williams