272 search results - page 5 / 55 » Parallel Reinforcement Learning with Linear Function Approxi... |
ICML
2008
IEEE
14 years 8 months ago
2008
IEEE
JMLR
2006
13 years 7 months ago
2006
In this article we describe a set of scalable techniques for learning the behavior of a group of agents in a collaborative multiagent setting. As a basis we use the framework of c...
ECAI
2008
Springer
13 years 9 months ago
2008
Springer
In many practical reinforcement learning problems, the state space is too large to permit an exact representation of the value function, much less the time required to compute it. ...
AUSAI
2005
Springer
14 years 1 months ago
2005
Springer
: In order to scale to problems with large or continuous state-spaces, reinforcement learning algorithms need to be combined with function approximation techniques. The majority of...
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...