Hi there,
I want to do the following:
- Collect multiple episodes (
"batch_mode": "complete_episodes"
) by workers and then - show metrics in Tensorboard in Episodes instead of
rollout_fragment_length
s. FYI: I have Episodes of fixed length of1,000ts
.
I have researched a little bit in the TBXLoggerCallback
and the CollectMetrics
classes, but have found so far no solution. Has anyone a hint for me?
To show you what I mean:
I ran 2 episodes with rollout_fragment_length=1000
(horizon=1000
) and with 2 workers. As a result I get a single point in Tensorboard for the single train iteration that used 2000
timesteps.
What I want to have is this:
no matter if I train with a single worker or 2 workers. This would be possible, if the frequency in Tensorboard would be based on episodes instead of training iterations.