  This technical report presents our 1st place solution for the Waymo Open Sim
Agents Challenge (WOSAC) 2023. Our proposed MultiVerse Transformer for Agent
simulation (MVTA) effectively leverages transformer-based motion prediction
approaches, and is tailored for closed-loop simulation of agents. In order to
produce simulations with a high degree of realism, we design novel training and
sampling methods, and implement a receding horizon prediction mechanism. In
addition, we introduce a variable-length history aggregation method to mitigate
the compounding error that can arise during closed-loop autoregressive
execution. On the WOSAC, our MVTA and its enhanced version MVTE reach a realism
meta-metric of 0.5091 and 0.5168, respectively, outperforming all the other
methods on the leaderboard.
