Neural-network-based parameter tuning for multi-agent simulation using deep reinforcement learning

This study proposes a new efficient parameter tuning method for multi-agent simulation (MAS) using deep reinforcement learning. MAS is currently a useful tool for social sciences, but is hard to realize realistic simulations due to its computational burden for parameter tuning. This study proposes efficient parameter tuning to address this issue using deep reinforcement learning methods. To improve compatibility with the tuning task, our proposed method employs actor-critic-based deep reinforcement learning, such as deep deterministic policy gradient (DDPG) and soft actor-critic (SAC). In addition to the customized version of DDPG and SAC for our task, we also propose three additional components to stabilize the learning: an action converter (DDPG only), a redundant full neural network actor, and a seed fixer. For experimental verification, we employ a parameter tuning task in an artificial financial market simulation, comparing our proposed model, its ablations, and the Bayesian estimation-based baseline. The results demonstrate that our model outperforms the baseline in terms of tuning performance, indicating that the additional components of the proposed method are essential. Moreover, the critic of our model works effectively as a surrogate model, that is, as an approximate function of the simulation, which allows the actor to tune the parameters appropriately. We have also found that the SAC-based method exhibits the best and fastest convergence, which we assume is achieved by the high exploration capability of SAC.