567 search results - page 8 / 114 » Regularized Policy Iteration |
ATAL
2009
Springer
14 years 2 months ago
2009
Springer
One of the key problems in reinforcement learning is balancing exploration and exploitation. Another is learning and acting in large or even continuous Markov decision processes (...
VALUETOOLS
2006
ACM
14 years 1 months ago
2006
ACM
Min-max functions are dynamic programming operators of zero-sum deterministic games with finite state and action spaces. The problem of computing the linear growth rate of the or...
Publication
Abstract: Several approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervis...
ISCC
2000
IEEE
13 years 12 months ago
2000
IEEE
With standard assumptions the routing and wavelength assignment problem (RWA) can be viewed as a Markov Decision Process (MDP). The problem, however, defies an exact solution bec...
ECML
2006
Springer
13 years 11 months ago
2006
Springer
Abstract. Approximate Policy Iteration (API) is a reinforcement learning paradigm that is able to solve high-dimensional, continuous control problems. We propose to exploit API for...