Dear all,
I’m both new to pyTorch and RL in general.
But recently I started to re-implement some of the most famous works.
I followed the tutorial of Denny Britz, but I used PyTorch to make it more interesting.
I found out that my implementation is much faster when run on the CPU than on the GPU, which is strange.
I’m not sure, but I suppose that this is cause by the fact that I need to move the GPU memory many times to the CPU to sample some actions.
You can find my code in the git repo: https://github.com/andompesta/MLTutorials/tree/master/RL/DeepQLearning 80
Is my intuition right? do you have nay suggestions? More importantly I found that there is no equivalent in pyTorch fo the function np.random.choice. Have anyone implemented it in torch?
Thanks in advance.
Cheers,
Sandro