181 search results - page 30 / 37 » On Policy Learning in Restricted Policy Spaces |
ATAL
2010
Springer
13 years 8 months ago
2010
Springer
Multi-agent learning is a crucial method to control or find solutions for systems, in which more than one entity needs to be adaptive. In today's interconnected world, such s...
ICML
1994
IEEE
13 years 11 months ago
1994
IEEE
Reinforcement learning (RL) algorithms provide a sound theoretical basis for building learning control architectures for embedded agents. Unfortunately all of the theory and much ...
NIPS
2003
13 years 9 months ago
2003
A mobile robot acting in the world is faced with a large amount of sensory data and uncertainty in its action outcomes. Indeed, almost all interesting sequential decision-making d...
UAI
1998
13 years 9 months ago
1998
tigate the use of temporally abstract actions, or macro-actions, in the solution of Markov decision processes. Unlike current models that combine both primitive actions and macro-...
IJRR
2008
13 years 7 months ago
2008
Designing distributed controllers for self-reconfiguring modular robots has been consistently challenging. We have developed a reinforcement learning approach which can be used bo...