  Temporal difference learning and Residual Gradient methods are the most
widely used temporal difference based learning algorithms; however, it has been
shown that none of their objective functions is optimal w.r.t approximating the
true value function $V$. Two novel algorithms are proposed to approximate the
true value function $V$. This paper makes the following contributions: (1) A
batch algorithm that can help find the approximate optimal off-policy
prediction of the true value function $V$. (2) A linear computational cost (per
step) near-optimal algorithm that can learn from a collection of off-policy
samples. (3) A new perspective of the emphatic temporal difference learning
which bridges the gap between off-policy optimality and off-policy stability.
