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CDC
2010
IEEE
160views Control Systems» more  CDC 2010»
15 years 2 months ago
Adaptive bases for Q-learning
Abstract-- We consider reinforcement learning, and in particular, the Q-learning algorithm in large state and action spaces. In order to cope with the size of the spaces, a functio...
Dotan Di Castro, Shie Mannor
JIPS
2010
152views more  JIPS 2010»
15 years 1 months ago
Medium Access Control with Dynamic Frame Length in Wireless Sensor Networks
Wireless sensor networks consist of sensor nodes which are expected to be battery-powered and are hard to replace or recharge. Thus, reducing the energy consumption of sensor nodes...
Dae-Suk Yoo, Seung Sik Choi
JMLR
2010
189views more  JMLR 2010»
15 years 1 months ago
Adaptive Step-size Policy Gradients with Average Reward Metric
In this paper, we propose a novel adaptive step-size approach for policy gradient reinforcement learning. A new metric is defined for policy gradients that measures the effect of ...
Takamitsu Matsubara, Tetsuro Morimura, Jun Morimot...
CAISE
2011
Springer
14 years 10 months ago
Supporting Dynamic, People-Driven Processes through Self-learning of Message Flows
Abstract. Flexibility and automatic learning are key aspects to support users in dynamic business environments such as value chains across SMEs or when organizing a large event. Pr...
Christoph Dorn, Schahram Dustdar
197
Voted
FOSSACS
2005
Springer
16 years 19 days ago
Model Checking for Nominal Calculi
Nominal calculi have been shown very effective to formally model a variety of computational phenomena. The models of nominal calculi have often infinite states, thus making model ...
Gian Luigi Ferrari, Ugo Montanari, Emilio Tuosto