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)
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Learning Dynamic Attribute-factored World Models for Efficient Multi-object Reinforcement Learning
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Often a task is a composition of previously learned tasks (e.g.
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In a single-agent setting, IL was shown to be done efficiently via an inverse soft-Q learning process.
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