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Study mode:
on
1
Intro
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Birds-eye view of RL
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Illustrative application: RL in personal health
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General thrust
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Direction: Exploiting structure in RL
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Vignette: Q-learning with low rank structure
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Vignette: Model-free versus model-based method
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Estimate dynamics or value functions for LQR? - Linear state space model with quadratic reward function
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Performance of LSTD versus model-based metho
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Direction: Exploration/exploitation beyond bandi
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Vignette: Q-learning with UCB
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Vignette: UCB and Monte Carlo Tree Search
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Direction: From worst-case to instance-optimalit
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Vignette: Instance-optimality of TD learning?
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Instance-optimality in policy evaluation
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Direction: RL in offline settings and causal inferen
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Some future directions exploiting methods from cal inferences instrumental variables propensity score, doubly robust methods, synthetic controls
Description:
Explore reinforcement learning in this 31-minute lecture by Martin Wainwright from UC Berkeley, presented at the Foundations of Data Science Institute Kickoff Workshop. Gain a bird's-eye view of RL and its application in personal health. Delve into exploiting structure in RL, including Q-learning with low rank structure and comparing model-free versus model-based methods. Examine the performance of LSTD versus model-based methods in linear state space models with quadratic reward functions. Investigate exploration/exploitation beyond bandits, including Q-learning with UCB and Monte Carlo Tree Search. Consider the concept of instance-optimality in RL, particularly in policy evaluation. Finally, explore RL in offline settings and its connections to causal inference, touching on instrumental variables, propensity scores, doubly robust methods, and synthetic controls.

Reinforcement Learning

Simons Institute
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