no code implementations • 15 Jan 2024 • Shuze Liu, Shuhang Chen, Shangtong Zhang
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.
no code implementations • 31 Jan 2023 • Shuze Liu, Shangtong Zhang
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.