181 search results - page 15 / 37 » On Policy Learning in Restricted Policy Spaces |
NIPS
2007
13 years 9 months ago
2007
Learning in real-world domains often requires to deal with continuous state and action spaces. Although many solutions have been proposed to apply Reinforcement Learning algorithm...
GECCO
2006
Springer
13 years 11 months ago
2006
Springer
In reinforcement learning, an agent interacting with its environment strives to learn a policy that specifies, for each state it may encounter, what action to take. Evolutionary c...
AUSAI
1999
Springer
13 years 12 months ago
1999
Springer
Abstract. Q-learning can be used to learn a control policy that maximises a scalar reward through interaction with the environment. Qlearning is commonly applied to problems with d...
IROS
2008
IEEE
14 years 2 months ago
2008
IEEE
Abstract— As robots become more commonplace within society, the need for tools to enable non-robotics-experts to develop control algorithms, or policies, will increase. Learning ...
ATAL
2009
Springer
14 years 2 months ago
2009
Springer
Creating coordinated multiagent policies in environments with uncertainty is a challenging problem, which can be greatly simplified if the coordination needs are known to be limi...