DQN behaves differently on different computers
I assume you run a certain version of your code with a given hyper-parameter values. Then, you need to fix random seed in the beginning of your code for tensorflow (e.g. tf.set_random_seed(1)), for numpy (e.g. np.random.seed(1)) and for random, if you use it. 
Additionally, you have to have same version of tensorflow on all you machines. I had a experience that even the forward pass between 1.3 and 1.8 resulted in two different outputs. Same check is required for gym. 
Finally, you have to check with either cpu or gpu. You cannot compare the results of a cpu run with a gpu run. 
If neither of these checks worked, I can check your colab code if you want to share it.
I have a more or less standard implementation of DQN solving the Atari "Breakout" (from Coursera Reinforcement learning course), that behaves totally different on different computers:

on my Laptop it converges each time I run it
on Coursera and Google Colab servers it never converges!

I use

Python3
Tensorflow
Kerass (only for Conv2D, Flatten and Dense layers)

I already spend some two weeks on the issue without any progress :(
I already checked:

The versions:


Python: same (3.6.7)
Tensorflow: same (tested with 1.4.0 and 1.5.0)
numpy: same up the bugfix number (1.16.2 vs 1.16.3)

Random seeds
float32 vs float64: I always pass dtype=np.float32 to each np.array and tf.placeholder call.
CPU/GPU


My laptop, that converges, uses old CPU (that limits Tensorflow to <= 1.5.0)
On Coursera server, that never converges: CPU?
On Google Colab server, that never converges: GPU


My questions here are:

What may be cause of the different behavior?
How such problems get debugged?
What can I also do/check to finally find the problem?

Update: All the code (incl. hyperparameters, env, ...) is exactly the same.