The paper proposes a temperature-parameterized entropy model for parking occupancy and a dynamic-programming policy (TIPP) that outperforms two simple policies in a simulated garage.
Online parking assignment in an environment of partially connected vehicles: A multi- agent deep reinforcement learning approach,
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Entropy-Based Dynamic Programming for Efficient Vehicle Parking
The paper proposes a temperature-parameterized entropy model for parking occupancy and a dynamic-programming policy (TIPP) that outperforms two simple policies in a simulated garage.