82 search results - page 11 / 17 » Practical Reinforcement Learning in Continuous Spaces |
ECAI
2008
Springer
13 years 10 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. ...
ML
2008
ACM
13 years 8 months ago
2008
ACM
Abstract. We consider batch reinforcement learning problems in continuous space, expected total discounted-reward Markovian Decision Problems. As opposed to previous theoretical wo...
MICAI
2010
Springer
13 years 7 months ago
2010
Springer
The full deployment of service robots in daily activities will require the robot to adapt to the needs of non-expert users, particularly, to learn how to perform new tasks from “...
ICAC
2008
IEEE
14 years 3 months ago
2008
IEEE
—Large scale production grids are an important case for autonomic computing. They follow a mutualization paradigm: decision-making (human or automatic) is distributed and largely...
ATAL
2009
Springer
14 years 3 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 (...