Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation
The paper introduces a unified framework for estimating higher-order derivatives of value functions in model-agnostic meta-reinforcement learning. It achieves this by using off-policy evaluation methods and Taylor expansions to balance the trade-off between bias and variance, interpreting prior techniques as special cases.