82 search results - page 7 / 17 » Practical Reinforcement Learning in Continuous Spaces |
AIIDE
2006
13 years 10 months ago
2006
Reinforcement learning is an effective machine learning paradigm in domains represented by compact and discrete state-action spaces. In high-dimensional and continuous domains, ti...
ICSTM
2000
13 years 10 months ago
2000
Performance measurement systems based on the principle that "if you can't measure it, you can't manage it" reinforce a short-term culture by focussing on tangi...
ECML
2006
Springer
14 years 7 days ago
2006
Springer
Reinforcement learning in real-world domains suffers from three curses of dimensionality: explosions in state and action spaces, and high stochasticity. We present approaches that ...
NIPS
2003
13 years 10 months ago
2003
We exploit some useful properties of Gaussian process (GP) regression models for reinforcement learning in continuous state spaces and discrete time. We demonstrate how the GP mod...
AROBOTS
1999
13 years 8 months ago
1999
We use reinforcement learning (RL) to compute strategies for multiagent soccer teams. RL may pro t signi cantly from world models (WMs) estimating state transition probabilities an...