But when I run a training-session it seems that the actual training is never reached, because the samples-buffer seems to not fill up.
I let a session run for more than an hour, but all I got is continously
"Agent 0"
"Samples 0"
But with zero samples in the buffer rl._agent.py->_train() will neither gain condition "self._total_sample_count >= self.init_samples" nor "self.replay_buffer_initialized" and therefore will never reach train_step(). Also no logging happens, file stays complete empty.
Do you have any idea why I only got "Agent 0" no matter how many workers I use and why the agent or agents do not gather samples?
Hello,
thank you for sharing your work!
Running a pretrained policy work like a charm.
But when I run a training-session it seems that the actual training is never reached, because the samples-buffer seems to not fill up.
I let a session run for more than an hour, but all I got is continously "Agent 0" "Samples 0"
But with zero samples in the buffer rl._agent.py->_train() will neither gain condition "self._total_sample_count >= self.init_samples" nor "self.replay_buffer_initialized" and therefore will never reach train_step(). Also no logging happens, file stays complete empty.
Do you have any idea why I only got "Agent 0" no matter how many workers I use and why the agent or agents do not gather samples?
Thank you in advance, bannox