56 search results - page 7 / 12 » Q-Learning in Continuous State and Action Spaces |
WDAG
2007
Springer
15 years 5 months ago
2007
Springer
Consider a distributed network of n nodes that is connected to a global source of “beats”. All nodes receive the “beats” simultaneously, and operate in lock-step. A scheme ...
ICML
2007
IEEE
16 years 13 days ago
2007
IEEE
Basis functions derived from an undirected graph connecting nearby samples from a Markov decision process (MDP) have proven useful for approximating value functions. The success o...
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Publication
Abstract: Several approximate policy iteration schemes without value functions, which focus on policy representation using classifiers and address policy learning as a supervis...
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NIPS
2008
15 years 1 months ago
2008
Our setting is a Partially Observable Markov Decision Process with continuous state, observation and action spaces. Decisions are based on a Particle Filter for estimating the bel...
ICML
2004
IEEE
16 years 13 days ago
2004
IEEE
Reinforcement learning deals with learning optimal or near optimal policies while interacting with the environment. Application domains with many continuous variables are difficul...