Task offloading, crucial for balancing computational loads across devices in networks such as the Internet of Things, poses significant optimization challenges, including minimizing latency and energy usage under strict communication and storage constraints. While traditional optimization falls short in scalability; and heuristic approaches lack in achieving optimal outcomes, Reinforcement Learning (RL) offers a promising avenue by enabling the learning of optimal offloading strategies through iterative interactions. However, the efficacy of RL hinges on access to rich datasets and custom-tailored, realistic training environments. To address this, we introduce PeersimGym, an open-source, customizable simulation environment tailored for developing and optimizing task offloading strategies within computational networks. PeersimGym supports a wide range of network topologies and computational constraints and integrates a \textit{PettingZoo}-based interface for RL agent deployment in both solo and multi-agent setups. Furthermore, we demonstrate the utility of the environment through experiments with Deep Reinforcement Learning agents, showcasing the potential of RL-based approaches to significantly enhance offloading strategies in distributed computing settings. PeersimGym thus bridges the gap between theoretical RL models and their practical applications, paving the way for advancements in efficient task offloading methodologies.

中文翻译：

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PeersimGym：通过强化学习解决任务卸载问题的环境

任务卸载对于平衡物联网等网络中设备之间的计算负载至关重要，它带来了重大的优化挑战，包括在严格的通信和存储限制下最大限度地减少延迟和能源使用。传统优化在可扩展性方面存在不足；由于启发式方法无法实现最佳结果，强化学习 (RL) 提供了一条有前途的途径，通过迭代交互来学习最佳卸载策略。然而，强化学习的功效取决于对丰富数据集和定制的真实训练环境的访问。为了解决这个问题，我们推出了 PeersimGym，这是一个开源、可定制的模拟环境，专为开发和优化计算网络中的任务卸载策略而设计。 PeersimGym 支持广泛的网络拓扑和计算约束，并集成了基于 \textit{PettingZoo} 的接口，用于在单代理和多代理设置中部署 RL 代理。此外，我们通过深度强化学习代理的实验展示了该环境的实用性，展示了基于强化学习的方法在分布式计算设置中显着增强卸载策略的潜力。因此，PeersimGym 弥合了理论 RL 模型与其实际应用之间的差距，为高效任务卸载方法的进步铺平了道路。