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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bb57db42f77807a9c5823bd8c2d9aaef-Paper.pdf
Neural Information Processing Systems
We study the reinforcement learning problem for discounted Markov Decision Processes(MDPs)underthetabularsetting.
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Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems
Neural Information Processing Systems
We present an application of the connectedness of these superlevel sets to the derivation of minimax theorems for robust reinforcement learning.
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Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes Yi Tian
Neural Information Processing Systems
We study minimax optimal reinforcement learning in episodic factored Markov decision processes (FMDPs), which are MDPs with conditionally independent transition components.
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