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I am coding a problem with the Actor-Critic Method. The final loss is a summation of PolicyLoss and ValueLoss. The calculation of the PolicyLoss for each step is given at Equation Number 5 of https://arxiv.org/pdf/1707.06347.pdf. And for Valueloss:

$V_t = \gamma \times V_{t+1} + r_t$

$\mbox{Valueloss} = \mbox{L}1\_\mbox{Loss}(Vt, Value)$

I checked that the PolicyLoss depends on this 'Value' as well. And PolicyLoss is decreasing, and so the parts of my AI model depending on the 'Value' are also updating. But I am seeing that the ValueLoss is not decreasing, however, the Total Loss which is the sum(ValueLoss, PolicyLoss) is decreasing.

I don't understand how to make the ValueLoss decrease, or if it should decrease at all. Because, the equation of ValueLoss suggests that it is like a 'chasing the tail' situation, so it may fluctuate.

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  • $\begingroup$ Please, provide the correct link to the paper you're referring to. I believe that's https://arxiv.org/abs/1707.06347. $\endgroup$
    – nbro
    Commented Feb 26, 2022 at 10:05
  • $\begingroup$ Yes, sorry, I edited now $\endgroup$ Commented Feb 26, 2022 at 10:08

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So it seems I was right. The ValueLoss does not decrease as it IS a 'chasing the tail situation'. If I run the code with ValueLoss = a constant - Value instead of NextValue - Value, I see that the loss decreases. A way out of it can be to calculate NextValue not for every run but for some runs.

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    $\begingroup$ Why would you want a "way out of it"? The reason the critic is "chasing its tail" is at least partially because the actor is improving, so the target values are really changing. What this does mean is that you should have different expectations when monitoring the critic loss compared to monitoring a supervised learning experiment. $\endgroup$ Commented Feb 26, 2022 at 19:38
  • $\begingroup$ Yes you are absolutely right. $\endgroup$ Commented Feb 28, 2022 at 11:18

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