43 search results - page 7 / 9 » The O.D.E. Method for Convergence of Stochastic Approximatio... |
ICML
2009
IEEE
14 years 9 months ago
2009
IEEE
Reinforcement Learning methods for controlling stochastic processes typically assume a small and discrete action space. While continuous action spaces are quite common in real-wor...
ECML
2004
Springer
14 years 1 months ago
2004
Springer
Abstract. We investigate the problem of using function approximation in reinforcement learning where the agent’s policy is represented as a classifier mapping states to actions....
ECML
2005
Springer
14 years 2 months ago
2005
Springer
This paper investigates a novel model-free reinforcement learning architecture, the Natural Actor-Critic. The actor updates are based on stochastic policy gradients employing Amari...
ICML
1998
IEEE
14 years 9 months ago
1998
IEEE
Production scheduling, the problem of sequentially con guring a factory to meet forecasted demands, is a critical problem throughout the manufacturing industry. The requirement of...
SAB
2010
Springer
13 years 7 months ago
2010
Springer
Abstract. In this paper we study distributed online learning of locomotion gaits for modular robots. The learning is based on a stochastic approximation method, SPSA, which optimiz...