Title: Temporal Difference Learning with Neural Networks - Study of the Leakage Propagation Problem,
Abstract: Temporal-Difference learning (TD) [Sutton, 1988] with function approximation
can converge to solutions that are worse than those obtained by Monte-Carlo
regression, even in the simple case of on-policy evaluation. To increase our
understanding of the problem, we investigate the issue of approximation errors
in areas of sharp discontinuities of the value function being further
propagated by bootstrap updates. We show empirical evidence of this leakage
propagation, and show analytically that it must occur, in a simple Markov
chain, when function approximation errors are present. For reversible policies,
the result can be interpreted as the tension between two terms of the loss
function that TD minimises, as recently described by [Ollivier, 2018]. We show
that the upper bounds from [Tsitsiklis and Van Roy, 1997] hold, but they do not
imply that leakage propagation occurs and under what conditions. Finally, we
test whether the problem could be mitigated with a better state representation,
and whether it can be learned in an unsupervised manner, without rewards or
privileged information.