  We investigate whether Jacobi preconditioning, accounting for the bootstrap
term in temporal difference (TD) learning, can help boost performance of
adaptive optimizers. Our method, TDprop, computes a per parameter learning rate
based on the diagonal preconditioning of the TD update rule. We show how this
can be used in both $n$-step returns and TD($\lambda$). Our theoretical
findings demonstrate that including this additional preconditioning information
is, surprisingly, comparable to normal semi-gradient TD if the optimal learning
rate is found for both via a hyperparameter search. In Deep RL experiments
using Expected SARSA, TDprop meets or exceeds the performance of Adam in all
tested games under near-optimal learning rates, but a well-tuned SGD can yield
similar improvements -- matching our theory. Our findings suggest that Jacobi
preconditioning may improve upon typical adaptive optimization methods in Deep
RL, but despite incorporating additional information from the TD bootstrap
term, may not always be better than SGD.
