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ATAL
2007
Springer
16 years 13 days ago
Reducing the complexity of multiagent reinforcement learning
It is known that the complexity of the reinforcement learning algorithms, such as Q-learning, may be exponential in the number of environment’s states. It was shown, however, th...
Andriy Burkov, Brahim Chaib-draa
GECCO
2009
Springer
162views Optimization» more  GECCO 2009»
15 years 4 months ago
Uncertainty handling CMA-ES for reinforcement learning
The covariance matrix adaptation evolution strategy (CMAES) has proven to be a powerful method for reinforcement learning (RL). Recently, the CMA-ES has been augmented with an ada...
Verena Heidrich-Meisner, Christian Igel
CSE
2009
IEEE
16 years 1 months ago
Reinforcement Learning of Listener Response for Mood Classification of Audio
This paper describes a method of applying a reinforcement learning artificial intelligence to categorize audio files by mood based on listener response during a performance. The s...
Jack Stockholm, Philippe Pasquier
AI
2006
Springer
15 years 10 months ago
Trace Equivalence Characterization Through Reinforcement Learning
In the context of probabilistic verification, we provide a new notion of trace-equivalence divergence between pairs of Labelled Markov processes. This divergence corresponds to the...
Josee Desharnais, François Laviolette, Kris...
AAAI
2007
15 years 8 months ago
A Reinforcement Learning Algorithm with Polynomial Interaction Complexity for Only-Costly-Observable MDPs
An Unobservable MDP (UMDP) is a POMDP in which there are no observations. An Only-Costly-Observable MDP (OCOMDP) is a POMDP which extends an UMDP by allowing a particular costly a...
Roy Fox, Moshe Tennenholtz