119 search results - page 13 / 24 » Inter-Task Action Correlation for Reinforcement Learning Tas... |
ICML
2001
IEEE
14 years 8 months ago
2001
IEEE
Hierarchical reinforcement learning (RL) is a general framework which studies how to exploit the structure of actions and tasks to accelerate policy learning in large domains. Pri...
ICML
2003
IEEE
14 years 8 months ago
2003
IEEE
An important issue in reinforcement learning is how to incorporate expert knowledge in a principled manner, especially as we scale up to real-world tasks. In this paper, we presen...
KCAP
2009
ACM
14 years 2 months ago
2009
ACM
As computational learning agents move into domains that incur real costs (e.g., autonomous driving or financial investment), it will be necessary to learn good policies without n...
TSMC
2008
13 years 7 months ago
2008
This paper describes several ensemble methods that combine multiple different reinforcement learning (RL) algorithms in a single agent. The aim is to enhance learning speed and fin...
NIPS
1993
13 years 9 months ago
1993
While exploring to nd better solutions, an agent performing online reinforcement learning (RL) can perform worse than is acceptable. In some cases, exploration might have unsafe, ...