43 search results - page 4 / 9 » The O.D.E. Method for Convergence of Stochastic Approximatio... |
ATAL
2007
Springer
14 years 2 months ago
2007
Springer
Reinforcement learning promises a generic method for adapting agents to arbitrary tasks in arbitrary stochastic environments, but applying it to new real-world problems remains di...
ICML
2001
IEEE
14 years 9 months ago
2001
IEEE
We introduce the first algorithm for off-policy temporal-difference learning that is stable with linear function approximation. Off-policy learning is of interest because it forms...
CDC
2010
IEEE
13 years 3 months ago
2010
IEEE
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...
NIPS
2008
13 years 10 months ago
2008
In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function approximation scheme we propose the use o...
AAAI
2012
11 years 11 months ago
2012
Markov decision processes (MDPs) are an established framework for solving sequential decision-making problems under uncertainty. In this work, we propose a new method for batchmod...