用JAX加速电动车充电模拟，让强化学习训练快100倍以上
Chargax: A JAX Accelerated EV Charging Simulator
- 基于JAX构建真实充电桩场景的可扩展模拟环境
- 相比传统环境提速100至1000倍，支持高效强化学习训练
- 模块化设计适配多种实际充电桩配置，适合能源系统研究者
深度强化学习在应对可持续能源挑战中具有关键作用，例如电网严重拥堵凸显了提升运行效率的迫切需求。然而，传统强化学习方法因样本复杂度高和仿真成本昂贵而进展缓慢。尽管近期工作通过将环境转换为JAX并利用GPU加速数据生成取得了进展，但主要集中在经典简化问题。本文提出Chargax，一个基于JAX的现实电动汽车充电站模拟环境，旨在加速强化学习智能体的训练。我们在基于真实数据的多种场景中验证该环境，对比强化学习智能体与基线方法的表现。Chargax实现了超过100x-1000x的计算性能提升。此外，其模块化架构可灵活表示多样化的现实充电站配置。
原文摘要 · Abstract (English)
Deep Reinforcement Learning can play a key role in addressing sustainable energy challenges. For instance, many grid systems are heavily congested, highlighting the urgent need to enhance operational efficiency. However, reinforcement learning approaches have traditionally been slow due to the high sample complexity and expensive simulation requirements. While recent works have effectively used GPUs to accelerate data generation by converting environments to JAX, these works have largely focussed on classical toy problems. This paper introduces Chargax, a JAX-based environment for realistic simulation of electric vehicle charging stations designed for accelerated training of RL agents. We validate our environment in a variety of scenarios based on real data, comparing reinforcement learning agents against baselines. Chargax delivers substantial computational performance improvements of over 100x-1000x over existing environments. Additionally, Chargax' modular architecture enables the representation of diverse real-world charging station configurations.
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