Car Following Model and Algorithm Design based on Reinforcement Learning

Abstract Based on reinforcement learning technology, this paper establishes a new driverless car following model. DQN algorithm and traffic simulator are mainly used to train the agent, and the following model is finally obtained. Under the precise and controllable experimental environment, the preset optimization targets can achieve the expected assumption and complete the following behavior. This study will contribute to the development of unmanned vehicles in the future.

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Share Market Prediction using Deep Neural Network

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.c6447.098319 ◽

2019 ◽

Vol 8 (3) ◽

pp. 8619-8622

Keyword(s):

Neural Network ◽

Machine Learning ◽

Deep Learning ◽

Reinforcement Learning ◽

Stock Market ◽

Deep Neural Network ◽

Learning Technology ◽

Financial Investment ◽

The Future ◽

Future Prediction

People, due to their complexity and volatile actions, are constantly faced with challenges in understanding the situation in the market share and the forecast for the future. For any financial investment, the stock market is a very important aspect. It is necessary to study while understanding the price fluctuations of the stock market. In this paper, the stock market prediction model using the Recurrent Digital natural Network (RDNN) is described. The model is designed using two important machine learning concepts: the recurrent neural network (RNN), multilayer perceptron (MLP) and reinforcement learning (RL). Deep learning is used to automatically extract important functions of the stock market; reinforcement learning of these functions will be useful for future prediction of the stock market, the system uses historical stock market data to understand the dynamic market behavior when you make decisions in an unknown environment. In this paper, the understanding of the dynamic stock market and the deep learning technology for predicting the price of the future stock market are described.

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A Deep Reinforcement Learning Based Car Following Model for Electric Vehicle

智能城市应用 ◽

10.33142/sca.v2i5.813 ◽

2019 ◽

Vol 2 (5) ◽

Author(s):

Yuankai Wu ◽

Huachun Tan ◽

Jiankun Peng ◽

Bin Ran

Keyword(s):

Reinforcement Learning ◽

Electric Vehicle ◽

Electricity Consumption ◽

Trust Region ◽

Research Area ◽

Data Driven ◽

Car Following ◽

Car Following Model ◽

On The Road ◽

Policy Optimization

Car following (CF) models are an appealing research area because they fundamentally describe longitudinal interactions of vehicles on the road, and contribute significantly to an understanding of traffic flow. There is an emerging trend to use data-driven method to build CF models. One challenge to the data-driven CF models is their capability to achieve optimal longitudinal driven behavior because a lot of bad driving behaviors will be learnt from human drivers by the supervised learning manner. In this study, by utilizing the deep reinforcement learning (DRL) techniques trust region policy optimization (TRPO), a DRL based CF model for electric vehicle (EV) is built. The proposed CF model can learn optimal driving behavior by itself in simulation. The experiments on following standard driving cycle show that the DRL model outperforms the traditional CF model in terms of electricity consumption.

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