17 search results - page 2 / 4 » Scaling Up Multi-agent Reinforcement Learning in Complex Dom... |
ECML
2006
Springer
13 years 9 months 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 ...
ATAL
2009
Springer
14 years 5 days ago
2009
Springer
Improving the sample efficiency of reinforcement learning algorithms to scale up to larger and more realistic domains is a current research challenge in machine learning. Model-ba...
ILP
2000
Springer
13 years 9 months ago
2000
Springer
Hierarchical reinforcement learning has been proposed as a solution to the problem of scaling up reinforcement learning. The RLTOPs Hierarchical Reinforcement Learning System is an...
ATAL
2009
Springer
14 years 5 days ago
2009
Springer
Multi-Agent Reinforcement Learning (MARL) algorithms suffer from slow convergence and even divergence, especially in largescale systems. In this work, we develop an organization-b...
ICDM
2006
IEEE
13 years 11 months ago
2006
IEEE
Many information integration tasks require computing similarity between pairs of objects. Pairwise similarity computations are particularly important in record linkage systems, as...