Multi-Agent Reinforcement Learning (MARL) is a promising candidate for
realizing efficient control of microscopic particles, of which micro-robots are
a subset. However, the microscopic particles' environment presents unique
challenges, such as Brownian motion at sufficiently small length-scales. In
this work, we explore the role of temperature in the emergence and efficacy of
strategies in MARL systems using particle-based Langevin molecular dynamics
simulations as a realistic representation of micro-scale environments. To this
end, we perform experiments on two different multi-agent tasks in microscopic
environments at different temperatures, detecting the source of a concentration
gradient and rotation of a rod. We find that at higher temperatures, the RL
agents identify new strategies for achieving these tasks, highlighting the
importance of understanding this regime and providing insight into optimal
training strategies for bridging the generalization gap between simulation and
reality. We also introduce a novel Python package for studying microscopic
agents using reinforcement learning (RL) to accompany our results.

本文采用 Langevin 分子动力学模拟，探究了温度在微观环境下多智能体强化学习中策略出现及有效性方面的影响，发现在高温下智能体能够发现新的任务完成策略，并介绍了一个用于研究微观智能体强化学习的 Python 包。