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

O$^2$TD: (Near)-Optimal Off-Policy TD Learning