با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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Dispatch of autonomous vehicles for taxi services: A deep reinforcement learning approach
ارسال وسایل نقلیه خودمختار برای خدمات تاکسی: یک رویکرد یادگیری تقویتی عمیق-2020 In this paper, we define and investigate a novel model-free deep reinforcement learning framework
to solve the taxi dispatch problem. The framework can be used to redistribute vehicles
when the travel demand and taxi supply is either spatially or temporally imbalanced in a
transportation network. While previous works mostly focus on using model-based methods, the
goal of this paper is to explore the policy-based deep reinforcement learning algorithm as a
model-free method to optimize the rebalancing strategy. In particular, we propose an actor-critic
algorithm with feed-forward neural networks as approximations of both policy and value functions,
where the policy function provides the optimal dispatch strategy and the value function
estimates the expected costs at each time stamp. Our numerical studies show that the algorithm
converges to the theoretical upper bound with less than 4% optimality gap, whether the system
dynamics are deterministic or stochastic. We also investigate the scenario where we consider user
priority and fairness, and the results indicate that our learned policy is capable of producing a
superior strategy that balances equity, cancellation, and level of service when user priority is
considered. Keywords: Taxi dispatching | Demand rebalancing | Deep reinforcement learning | Actor-critic algorithm | Policy-gradient | Autonomous vehicles | Ride sharing |
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