First pick the best actions according to the visit count. Then, unroll the environment by applying these actions to the environment obtaining the rewards needed to train.

University of Michigan - Ann Arbor

They joined the reward loss and value loss into one component since their values are intertwined because they calculate the value based on the rewards. And they set hard weights and combine loss for real game trajectory and loss for greedy trajectory from simulated games.

How to combine on-policy and off-policy strategy of MuZero?

Borges, Alexandre and Arlindo L. Oliveira. “Combining Off and On-Policy Training in Model-Based Reinforcement Learning.” ArXiv abs/2102.12194 (2021): n. pag.
https://arxiv.org/pdf/2102.12194.pdf

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