7280 search results - page 1 / 1456 » Adaptive bases for Q-learning |
IROS
2008
IEEE
13 years 10 months ago
2008
IEEE
—Multi-robot reinforcement learning is a very challenging area due to several issues, such as large state spaces, difficulty in reward assignment, nondeterministic action selecti...
NIPS
2003
13 years 5 months ago
2003
Recent multi-agent extensions of Q-Learning require knowledge of other agents’ payoffs and Q-functions, and assume game-theoretic play at all times by all other agents. This pap...
ICML
1998
IEEE
14 years 5 months ago
1998
IEEE
This paper presents a new approach to hierarchical reinforcement learning based on the MAXQ decomposition of the value function. The MAXQ decomposition has both a procedural seman...
HCI
2009
13 years 2 months ago
2009
This paper describes the framework and development process of adaptive user interfaces within the OASIS project. After presenting a rationale for user interface adaptation to addre...
ICRA
2002
IEEE
13 years 9 months ago
2002
IEEE
Learning can be an effective way for robot systems to deal with dynamic environments and changing task conditions. However, popular singlerobot learning algorithms based on discou...