This directory contains an adapted version of the official implementation of CURL .
To train a CURL agent on the SoftGym tasks, run
python curl/run_curl.py
See curl/run_curl.py for different arguments, such as the task to run and the observation space.
In your console, you should see printouts that look like:
| train | E: 221 | S: 28000 | D: 18.1 s | R: 785.2634 | BR: 3.8815 | A_LOSS: -305.7328 | CR_LOSS: 190.9854 | CU_LOSS: 0.0000
| train | E: 225 | S: 28500 | D: 18.6 s | R: 832.4937 | BR: 3.9644 | A_LOSS: -308.7789 | CR_LOSS: 126.0638 | CU_LOSS: 0.0000
| train | E: 229 | S: 29000 | D: 18.8 s | R: 683.6702 | BR: 3.7384 | A_LOSS: -311.3941 | CR_LOSS: 140.2573 | CU_LOSS: 0.0000
| train | E: 233 | S: 29500 | D: 19.6 s | R: 838.0947 | BR: 3.7254 | A_LOSS: -316.9415 | CR_LOSS: 136.5304 | CU_LOSS: 0.0000
Log abbreviation mapping:
train - training episode
E - total number of episodes
S - total number of environment steps
D - duration in seconds to train 1 episode
R - mean episode reward
BR - average reward of sampled batch
A_LOSS - average loss of actor
CR_LOSS - average loss of critic
CU_LOSS - average loss of the CURL encoder