You can change the environment configuration as you like and we will use the same during the training phase. During the rollouts, we will force the following configuration
As long as your code uses
rllib, things should work. You can add more environment variables or arguments to
python train.py line in
run.sh but we will still use the
train.py wrapper that we provided to trigger the training. So any changes you make to
train.py will be dropped.
This discussion should give you more context on why we want to enforce the use of a framework, FAQ: Regarding rllib based approach for submissions
All the best for the competition!