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SIAMCO
2000
117views more  SIAMCO 2000»
14 years 10 months ago
The O.D.E. Method for Convergence of Stochastic Approximation and Reinforcement Learning
It is shown here that stability of the stochastic approximation algorithm is implied by the asymptotic stability of the origin for an associated ODE. This in turn implies convergen...
Vivek S. Borkar, Sean P. Meyn
78
Voted
GECCO
2010
Springer
169views Optimization» more  GECCO 2010»
15 years 2 days ago
Stochastic local search in continuous domains: questions to be answered when designing a novel algorithm
Several population-based methods (with origins in the world of evolutionary strategies and estimation-of-distribution algorithms) for black-box optimization in continuous domains ...
Petr Posik
NIPS
1993
14 years 11 months ago
Optimal Stochastic Search and Adaptive Momentum
Stochastic optimization algorithms typically use learning rate schedules that behave asymptotically as (t) = 0=t. The ensemble dynamics (Leen and Moody, 1993) for such algorithms ...
Todd K. Leen, Genevieve B. Orr
DAM
2011
14 years 5 months ago
An integer L-shaped algorithm for the Dial-a-Ride Problem with stochastic customer delays
This paper considers a single-vehicle Dial-a-Ride problem in which customers may experience stochastic delays at their pickup locations. If a customer is absent when the vehicle s...
Géraldine Heilporn, Jean-François Co...
ICAART
2010
INSTICC
15 years 7 months ago
Complexity of Stochastic Branch and Bound Methods for Belief Tree Search in Bayesian Reinforcement Learning
There has been a lot of recent work on Bayesian methods for reinforcement learning exhibiting near-optimal online performance. The main obstacle facing such methods is that in most...
Christos Dimitrakakis