Learning Graphical Models
A graphical model or probabilistic graphical model (PGM) or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. They are commonly used in probability theory, statistics—particularly Bayesian statistics—and machine learning. (Wikipedia)
Real-TimeRecurrentLearningusing TraceUnitsinReinforcementLearning
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A promising direction istouse linear recurrent architectures (LRUs), where dense recurrent weights are replaced with a complex-valued diagonal, making RTRL efficient.
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However,macro-action design isoften heuristic and rarely comes with performance guarantees.
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