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Questions about Multiple “Brains,” SetModel(), and Wall Jump example.

Discussion in 'ML-Agents' started by sarachan, Jun 2, 2022.

  1. sarachan


    Jul 21, 2013
    I would like to create two or more models for the same agent, using the same action and observation spaces. I have seen the Wall Jump example and how SetModel() is used to switch among 3 models.

    However, I am not sure how these 3 models were trained. Were they each trained individually, or were they somehow trained all together? The documentation for SetModel() says, “The model and inferenceDevice parameters are ignored when not using inference.” So this sounds as though SetModel() does not switch among the models while training, implying that the models were trained individually. Is that correct? And if so, are there typically different agent scripts for the different models, or a single script having different cases for the different models? (The rewards would be different, although the actions and observations are the same.)

    Also, there are two different behaviors in the Wall Jump example: SmallWallJump and BigWallJump. I see that these bring up different sets of parameters from the configuration yaml file. Are there any other effects of these different named behaviors?

    Thanks for any insights or clarifications!