论文标题
多机构环境的动态声音可以改善概括：基于代理的模型满足强化学习
Dynamic Noises of Multi-Agent Environments Can Improve Generalization: Agent-based Models meets Reinforcement Learning
论文作者
论文摘要
我们研究了基于基于代理模型（ABM）的增强学习（RL）环境的好处。虽然已知ABM以计算复杂性为代价提供了微观基础模拟，但我们在这项工作中表明，它们的非确定性动态可以改善RL剂的概括。为此，我们检查了基于微分方程或ABM的流行病SIR环境的控制。数值模拟表明，SIR模型的基于ABM的动力学中的固有噪声不仅可以改善平均奖励，还可以使RL药物概括在更广泛的流行参数范围内。
We study the benefits of reinforcement learning (RL) environments based on agent-based models (ABM). While ABMs are known to offer microfoundational simulations at the cost of computational complexity, we empirically show in this work that their non-deterministic dynamics can improve the generalization of RL agents. To this end, we examine the control of an epidemic SIR environments based on either differential equations or ABMs. Numerical simulations demonstrate that the intrinsic noise in the ABM-based dynamics of the SIR model not only improve the average reward but also allow the RL agent to generalize on a wider ranges of epidemic parameters.
