1760 search results - page 73 / 352 » Learning from Partial Observations |
125
Voted
COLT
1998
Springer
15 years 6 months ago
1998
Springer
Most of the work which attempts to give bounds on the generalization error of the hypothesis generated by a learning algorithm is based on methods from the theory of uniform conve...
125
Voted
AAAI
2010
15 years 4 months ago
2010
This paper introduces a principled approach for the design of a scalable general reinforcement learning agent. This approach is based on a direct approximation of AIXI, a Bayesian...
102
Voted
UAI
2001
15 years 4 months ago
2001
There exist a number of reinforcement learning algorithms which learn by climbing the gradient of expected reward. Their long-run convergence has been proved, even in partially ob...
121
Voted
AIMSA
2004
Springer
15 years 6 months ago
2004
Springer
Multi-agent reinforcement learning (MARL) is an emerging area of research. However, it lacks two important elements: a coherent view on MARL, and a well-defined problem objective. ...
112
Voted
CORR
2010
Springer
15 years 2 months ago
2010
Springer
Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously diffic...