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Problem with more than one action - A3C #55
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hello, Regards, |
Hi Akhil, It may be related to the activation function you selected. For example, if mapping action to (-1, 1), you will choose tanh as the mapping function and sigmoid for (0, 1), these two mappings have different derivative which may affect your training. |
hello Zhou, Thank you very much for the reply, I have tried the sigmoid activation function with [0,1] action bound, but it still get stuck in the local minima. But with sigmoid act. function and [-1,1] as action bound it again starts learning really well. Do u have some idea about it? regards, |
Then I think it is likely that the backprop with tanh is better than sigmoid. This might be one of the reasons. |
hello Zhou, But I am getting good results with sigmoid act. function and action bound of [-1, 1]. This makes me confused on how the action bound is really affecting even after using a sigmoid activation function. |
The calculation of action bound is |
thanks zhou |
Hello,
Thank you very much for the A3C implementation. I was trying implement your A3C implementation on biped walker to a custom muscle model and got it working for a single action output. But when I tried to implement it with more than one action, it get stuck in the smallest reward local minima. I tried starting the training with high learning rate and also tried different entropy_beta value to encourage more exploration. But alas nothing helped. could u help me with some advise on this.
Irrespective of whatever I tried the training get stuck in the local minima with a smallest reward possible.
regards.
akhil
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