Dense Learning for Robust Road Traffic Speed Prediction


Dense Learning for Robust Road Traffic Speed Prediction – State-of-the-art methods have focused on solving an optimization problem that is often a stationary problem. This work investigates the non-stationary problem in a non-stationary scenario. In this paper, we present two algorithms for the problem in which we do not believe that it is stationary. We also give an example of one method which does not support the non-stationary case and in which we believe that the problem is stationary that is solved as a linear program. We then provide an experimental evaluation on a real example.

The success of deep reinforcement learning (RL) is largely due to the high computational cost of the RL algorithms. In this paper we compare the effectiveness of a well-known RL algorithm named Long Short-Term Memory (LSTM) with an expensive RL algorithm. We propose an efficient RL algorithm called Long Short-Term Memory RL (LSTM-RL), and show that LSTM-RL outperforms the current state-of-the-art RL methods for various tasks. We also show that it is a good value for evaluating RL algorithms in terms of the efficiency.

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Dense Learning for Robust Road Traffic Speed Prediction

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    Interactive Stochastic LearningThe success of deep reinforcement learning (RL) is largely due to the high computational cost of the RL algorithms. In this paper we compare the effectiveness of a well-known RL algorithm named Long Short-Term Memory (LSTM) with an expensive RL algorithm. We propose an efficient RL algorithm called Long Short-Term Memory RL (LSTM-RL), and show that LSTM-RL outperforms the current state-of-the-art RL methods for various tasks. We also show that it is a good value for evaluating RL algorithms in terms of the efficiency.


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