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The ODE Method for Stochastic Approximation and Reinforcement Learning with Markovian Noise
Stochastic approximation is a class of algorithms that update a vector iteratively, incrementally, and stochastically, including, e. g., stochastic gradient descent and temporal difference learning.
Improving Monte Carlo Evaluation with Offline Data
Most reinforcement learning practitioners evaluate their policies with online Monte Carlo estimators for either hyperparameter tuning or testing different algorithmic design choices, where the policy is repeatedly executed in the environment to get the average outcome.
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