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ICML
2003
IEEE
15 years 10 months ago
Exploration in Metric State Spaces
We present metric?? , a provably near-optimal algorithm for reinforcement learning in Markov decision processes in which there is a natural metric on the state space that allows t...
Sham Kakade, Michael J. Kearns, John Langford
ECML
2007
Springer
15 years 1 months ago
Efficient Continuous-Time Reinforcement Learning with Adaptive State Graphs
Abstract. We present a new reinforcement learning approach for deterministic continuous control problems in environments with unknown, arbitrary reward functions. The difficulty of...
Gerhard Neumann, Michael Pfeiffer, Wolfgang Maass
PUC
1998
92views more  PUC 1998»
14 years 9 months ago
Transparent Migration of Java-Based Mobile Agents: Capturing and Reestablishing the State of Java Programs
In this paper we describe a way to save and restore the state of a running Java program. We achieve this on the language level, without modifying the Java virtual machine, by instr...
Stefan Fünfrocken
WSC
1997
14 years 11 months ago
Selective Rerouting Using Simulated Steady State System Data
Effective operational control of a manufacturing system that has routing flexibility is dependent upon being able to make informed real-time decisions in the event of a system dis...
Catherine M. Harmonosky, Robert H. Farr, Ming-Chua...
OOPSLA
2009
Springer
15 years 4 months ago
Typestate-oriented programming
Objects model the world, and state is fundamental to a faithful modeling. Engineers use state machines to understand and reason about state transitions, but programming languages ...
Jonathan Aldrich, Joshua Sunshine, Darpan Saini, Z...