average gradients to update global theta? about pytorch-dppo HOT 8 OPEN

@weicheng113 I think same as you think. In my code, I am adding the following division.

p._grad = shared_grad_buffers.grads[n+'_grad']/params.num_processes

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@yusukeurakami Thanks for the reply. Do you mean you are going to add the averaging in this line -

Or you have already added somewhere, which I did not find it. Thanks.

@yusukeurakami Sorry, I thought you were the author of the code. :) By the way, is the training working fine after you apply the division?

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@weicheng113 No problem. I replied to you because I was stacked at the same place. I don't have enough data points to compare the result yet, and I have to. I will update my result when I got it.

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@yusukeurakami Thanks a lot.

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@weicheng113 I've run my training with 7 workers in total. So, with average, gradients will be divided by 7 every update. however, from the result, both with average and non-average converged in the same values in almost same update steps. I don't really understand why it behaves same even the parameters were updated 7times smaller...

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@yusukeurakami Thanks for sharing good findings. I don't understand also. From gut feeling, the average will make update more steady with smaller steps. Could it be the env you are trying to solve is simple so that It cannot tell?

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@weicheng113 I am running a robot arm with 7 joints in continuous action and state space (original Mujoco environment). It should be complex enough.

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@yusukeurakami Ok, thanks.

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