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This paper proposes a Proximal Policy Optimization (PPO)-based deep reinforcement learning framework for dynamic battery charging of autonomous mobile robots in warehouses, achieving up to 6% higher order-completion rates over baseline methods.
This paper presents a multi-objective control system using differential evolution and multi-agent deep reinforcement learning for battery management in dairy farms, achieving up to 18% higher profits from energy arbitrage compared to rule-based models.
This paper introduces C2L-Net, a data-driven model for efficient and accurate state-of-charge estimation of lithium-ion batteries using short historical windows.