RE-SAC: Disentangling aleatoric and epistemic risks in bus fleet control: A stable and robust ensemble DRL approach
RE-SAC: Disentangling aleatoric and epistemic risks in bus fleet control: A stable and robust ensemble DRL approach explores A robust ensemble deep reinforcement learning framework that disentangles aleatoric and epistemic risks to improve bus fleet control in volatile environments.. Commercial viability score: 7/10 in Reinforcement Learning.
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