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NIPS
1993
13 years 8 months ago
Convergence of Stochastic Iterative Dynamic Programming Algorithms
Recent developments in the area of reinforcement learning have yielded a number of new algorithms for the prediction and control of Markovian environments. These algorithms,includ...
Tommi Jaakkola, Michael I. Jordan, Satinder P. Sin...
AAAI
2008
13 years 9 months ago
An Efficient Motion Planning Algorithm for Stochastic Dynamic Systems with Constraints on Probability of Failure
When controlling dynamic systems, such as mobile robots in uncertain environments, there is a trade off between risk and reward. For example, a race car can turn a corner faster b...
Masahiro Ono, Brian C. Williams
SIAMCO
2002
121views more  SIAMCO 2002»
13 years 7 months ago
Consistent Approximations and Approximate Functions and Gradients in Optimal Control
As shown in [7], optimal control problems with either ODE or PDE dynamics can be solved efficiently using a setting of consistent approximations obtained by numerical discretizati...
Olivier Pironneau, Elijah Polak
WINE
2007
Springer
124views Economy» more  WINE 2007»
14 years 1 months ago
Stochastic Models for Budget Optimization in Search-Based Advertising
Internet search companies sell advertisement slots based on users’ search queries via an auction. Advertisers have to solve a complex optimization problem of how to place bids o...
S. Muthukrishnan, Martin Pál, Zoya Svitkina
CDC
2010
IEEE
160views Control Systems» more  CDC 2010»
13 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