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#computer-science #machine-learning #reinforcement-learning
One error t h at is always observable is that between the value estimate at each time and the return from that time. The Mean Square Return Error, denoted RE , is the expectation, unde r µ , of the square of this error. In the on-policy case the RE can be written RE(w)=E h G t ˆv(S t ,w) 2 i = VE(w)+E h G t v ⇡ (S t ) 2 i . (11.24)
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