  Gradient-based methods for optimisation of objectives in stochastic settings
with unknown or intractable dynamics require estimators of derivatives. We
derive an objective that, under automatic differentiation, produces
low-variance unbiased estimators of derivatives at any order. Our objective is
compatible with arbitrary advantage estimators, which allows the control of the
bias and variance of any-order derivatives when using function approximation.
Furthermore, we propose a method to trade off bias and variance of higher order
derivatives by discounting the impact of more distant causal dependencies. We
demonstrate the correctness and utility of our objective in analytically
tractable MDPs and in meta-reinforcement-learning for continuous control.
