Danijar Hafner

I've seen it working on many people's computers :) Please check if YAML is installed: ```shell python3 -c "import ruamel.yaml; print('success')" ``` And check if the Pendulum environment works: ```shell...

Yea, that might be the problem. Processing is quite different between Windows and Linux/Mac and we mainly tested on the latter. I'm afraid I can't be of much help since...

@donamin Where you able to narrow down this issue?

Thanks for getting back. I'll keep this issue open for now. We might support Windows in the future since as far as I can see the threading is the only...

@erwincoumans Yes, this seems trivial since `self._worker()` does not access any object state. You'd just have to replace the occurrences of `self` with `ExternalProcess`. I'd be happy to accept a...

Hi @fengredrum. In case this is still an issue, could you try wrapping your network implementation in a `with tf.device('/gpu:0')` block?

Thanks for providing more details. I don't think the replay buffer should be placed on GPU, since it can grow quite large, especially when training from pixel observations. All ops...

@colinskow Could you try running without environment processes (`--noenv_processes`), please? When there is a crash in one of the processes it can cause the program to deadlock before anything is...

This is an interesting topic. I would imagine that a simple distribution pattern would be to run multiple instances of the current code on multiple machine, each simulating their own...

I think in many scenarios it makes sense to simulate and train on the same machine, and just scale the number of those machines. That's mainly because it seems to...