• I have marked all applicable categories:
  • exception-raising bug
  • RL algorithm bug
  • documentation request (i.e. "X is missing from the documentation.")
  • new feature request
• I have visited the source website, and in particular read the known issues
• I have searched through the issue tracker and issue categories for duplicates
• I have mentioned version numbers, operating system and environment, where applicable:

  import tianshou, torch, sys
  print(tianshou.__version__, torch.__version__, sys.version, sys.platform)

I was wondering how to handle learning starts. As far as I can tell from the code, the actions are sampled from the policy from the beginning. How would one start the learning with e.g. 1000 timesteps of uniformly sampled actions?