  Emphatic Temporal Difference (ETD) learning has recently been proposed as a
convergent off-policy learning method. ETD was proposed mainly to address
convergence issues of conventional Temporal Difference (TD) learning under
off-policy training but it is different from conventional TD learning even
under on-policy training. A simple counterexample provided back in 2017 pointed
to a potential class of problems where ETD converges but TD diverges. In this
paper, we empirically show that ETD converges on a few other well-known
on-policy experiments whereas TD either diverges or performs poorly. We also
show that ETD outperforms TD on the mountain car prediction problem. Our
results, together with a similar pattern observed under off-policy training in
prior works, suggest that ETD might be a good substitute over conventional TD.
