Question regarding locomotion tasks in a static environment where the environment-space (not just local) position of the agent is important. My agent was trained to move using the DecisionRequester, with a period of 5, and RequestActions in-between decisions. It uses CharacterController.SimpleMove in FixedUpdate using the output of the network. I am using the trained network in a separate project for releases, and put the SimpleMove in the Update loop using a global displacement vector updated in inference results every 5 FixedUpdate frames. So far it seems to be performing the task as it was trained to. In principle, the agent shouldn't be dependent on the time scale or update loop because it moves to get closer to some environment-space position, based on it's current position. It also has stacked information so it (might) have learned to recognize how it's actions influence its future state. Does this generalize across static locomotion tasks that are environment-dependent, with the reward function linked to moving to environment-space areas?