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@ -24,17 +24,17 @@ python train.py \
--work_dir ./log \ --work_dir ./log \
--seed 1 --seed 1
``` ```
This will produce a folder (`./save`) by default, where all the output is going to be stored including train/eval logs, tensorboard blobs, evaluation videos, and model snapshots. It is possible to attach tensorboard to a particular run using the following command: This will produce 'log' folder, where all the outputs are going to be stored including train/eval logs, tensorboard blobs, and evaluation episode videos. One can attacha tensorboard to monitor training by running:
``` ```
tensorboard --logdir save tensorboard --logdir log
``` ```
Then open up tensorboad in your browser. and opening up tensorboad in your browser.
You will also see some console output, something like this: The console output is also available in a form:
``` ```
| train | E: 1 | S: 1000 | D: 0.8 s | R: 0.0000 | BR: 0.0000 | ALOSS: 0.0000 | CLOSS: 0.0000 | RLOSS: 0.0000 | train | E: 1 | S: 1000 | D: 0.8 s | R: 0.0000 | BR: 0.0000 | ALOSS: 0.0000 | CLOSS: 0.0000 | RLOSS: 0.0000
``` ```
This line means: a training entry decodes as:
``` ```
train - training episode train - training episode
E - total number of episodes E - total number of episodes
@ -46,8 +46,7 @@ ALOSS - average loss of actor
CLOSS - average loss of critic CLOSS - average loss of critic
RLOSS - average reconstruction loss (only if is trained from pixels and decoder) RLOSS - average reconstruction loss (only if is trained from pixels and decoder)
``` ```
These are just the most important number, more of all other metrics can be found in tensorboard. and an evaluation entry :
Also, besides training, once in a while there is evaluation output, like this:
``` ```
| eval | S: 0 | ER: 21.1676 | eval | S: 0 | ER: 21.1676
``` ```