<ul><li>The article discusses the challenges faced in optimizing Electric Bus (EB) charging schedules due to uncertainties in travel time, energy consumption, and fluctuating electricity prices.</li><li>A solution proposed in the paper is the use of Hierarchical Deep Reinforcement Learning (HDRL) to reformulate the Markov Decision Process (MDP) into two augmented MDPs for efficient decision-making across multiple time scales.</li><li>The novel HDRL algorithm introduced, called Double Actor-Critic Multi-Agent Proximal Policy Optimization Enhancement (DAC-MAPPO-E), addresses scalability challenges for large EB fleets by redesigning the decentralized actor network and incorporating the Multi-Agent Proximal Policy Optimization (MAPPO) algorithm.</li><li>Extensive experiments with real-world data support the superior performance and scalability of DAC-MAPPO-E in optimizing EB fleet charging schedules.</li></ul>

Optimizing Electric Bus Charging Scheduling with Uncertainties Using Hierarchical Deep Reinforcement Learning

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