231 search results - page 24 / 47 » Active Learning in Partially Observable Markov Decision Proc... |
ICMLA
2009
13 years 6 months ago
2009
In sequential decision making under uncertainty, as in many other modeling endeavors, researchers observe a dynamical system and collect data measuring its behavior over time. The...
CORR
2010
Springer
13 years 8 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...
CORR
2010
Springer
13 years 7 months ago
2010
Springer
We consider model-based reinforcement learning in finite Markov Decision Processes (MDPs), focussing on so-called optimistic strategies. Optimism is usually implemented by carryin...
AAAI
2004
13 years 10 months ago
2004
The search for finite-state controllers for partially observable Markov decision processes (POMDPs) is often based on approaches like gradient ascent, attractive because of their ...
NIPS
2001
13 years 10 months ago
2001
Policy gradient methods for reinforcement learning avoid some of the undesirable properties of the value function approaches, such as policy degradation (Baxter and Bartlett, 2001...