Medium: It contributes to significant difficulty to complete my task, but I can work around it.
What is the best way to search different environment configurations using Tune?
I found the env_config key, val that can be passed in with the config option of tune.run(), where a tune.<search> algorithm can be used to search across various settings. However, I’m training a multi-agent environment where the policies need to have the observation and action space given to it before tune.run() is executed. Currently I’m achieving this by creating a sample environment just before tune.run() and extracting the sample_env.observation_space and sample_env.action_space.
Unless some input validation or something is happening, I’m assuming the policies only need to know about the shape of the observations/actions. However, if the shape changes with each environment configuration, there is a mismatch between policy and environment and an error is thrown.