Automated vehicle’s behavior decision making using deep reinforcement learning and high-fidelity simulation environment
تصمیم گیری خودکار وسیله نقلیه با استفاده از یادگیری تقویتی عمیق و محیط شبیه سازی با وفاداری بالا-2019
Automated vehicles (AVs) are deemed to be the key element for the intelligent transportation system in the future. Many studies have been made to improve AVs’ ability of environment recognition and vehicle control, while the attention paid to decision making is not enough and the existing decision algorithms are very preliminary. Therefore, a framework of the decisionmaking training and learning is put forward in this paper. It consists of two parts: the deep reinforcement learning (DRL) training program and the high-fidelity virtual simulation environment. Then the basic microscopic behavior, car-following (CF), is trained within this framework. In addition, theoretical analysis and experiments were conducted to evaluate the proposed reward functions for accelerating training using DRL. The results show that on the premise of driving comfort, the efficiency of the trained AV increases 7.9% and 3.8% respectively compared to the classical adaptive cruise control models, intelligent driver model and constant-time headway policy. Moreover, on a more complex three-lane section, we trained an integrated model combining both CF and lane-changing behavior, with the average speed further growing 2.4%. It indicates that our framework is effective for AV’s decision-making learning.
Keywords: Automated vehicle | Decision making | Deep reinforcement learning | Reward function
(Deep) Reinforcement learning for electric power system control and related problems: A short review and perspectives
(عمیق) یادگیری تقویتی برای کنترل سیستم برق و مشکلات مرتبط با آن: یک مرور کوتاه و چشم اندازها-2019
This paper reviews existing works on (deep) reinforcement learning considerations in electric power sys- tem control. The works are reviewed as they relate to electric power system operating states (normal, preventive, emergency, restorative) and control levels (local, household, microgrid, subsystem, wide-area). Due attention is paid to the control-related problems considerations (cyber-security, big data analysis, short-term load forecast, and composite load modelling). Observations from reviewed literature are drawn and perspectives discussed. In order to make the text compact and as easy as possible to read, the focus is only on the works published (or “in press”) in journals and books while conference publications are not included. Exceptions are several work available in open repositories likely to become journal pub- lications in near future. Hopefully this paper could serve as a good source of information for all those interested in solving similar problems.
Keywords: Electric power system | Reinforcement learning | Deep reinforcement learning | Control | Control-related problems
Intelligent fault diagnosis for rotating machinery using deep Q-network based health state classification: A deep reinforcement learning approach
تشخیص خطای هوشمند برای ماشین آلات در حال چرخش با استفاده از طبقه بندی حالت سلامت مبتنی بر شبکه Q عمقی: یک روش یادگیری تقویتی عمیق-2019
Fault diagnosis methods for rotating machinery have always been a hot research topic, and artificial intelligencebased approaches have attracted increasing attention from both researchers and engineers. Among those related studies and methods, artificial neural networks, especially deep learning-based methods, are widely used to extract fault features or classify fault features obtained by other signal processing techniques. Although such methods could solve the fault diagnosis problems of rotating machinery, there are still two deficiencies. (1) Unable to establish direct linear or non-linear mapping between raw data and the corresponding fault modes, the performance of such fault diagnosis methods highly depends on the quality of the extracted features. (2) The optimization of neural network architecture and parameters, especially for deep neural networks, requires considerable manual modification and expert experience, which limits the applicability and generalization of such methods. As a remarkable breakthrough in artificial intelligence, AlphaGo, a representative achievement of deep reinforcement learning, provides inspiration and direction for the aforementioned shortcomings. Combining the advantages of deep learning and reinforcement learning, deep reinforcement learning is able to build an end-to-end fault diagnosis architecture that can directly map raw fault data to the corresponding fault modes. Thus, based on deep reinforcement learning, a novel intelligent diagnosis method is proposed that is able to overcome the shortcomings of the aforementioned diagnosis methods. Validation tests of the proposed method are carried out using datasets of two types of rotating machinery, rolling bearings and hydraulic pumps, which contain a large number of measured raw vibration signals under different health states and working conditions. The diagnosis results show that the proposed method is able to obtain intelligent fault diagnosis agents that can mine the relationships between the raw vibration signals and fault modes autonomously and effectively. Considering that the learning process of the proposed method depends only on the replayed memories of the agent and the overall rewards, which represent much weaker feedback than that obtained by the supervised learning-based method, the proposed method is promising in establishing a general fault diagnosis architecture for rotating machinery.
Keywords: Fault diagnosis | Rotating machinery | Deep reinforcement learning | Deep Q-network
Decentralized network level adaptive signal control by multi-agent deep reinforcement learning
کنترل سیگنال تطبیقی سطح شبکه غیر متمرکز با یادگیری تقویت عمیق چند عاملی-2019
Adaptive traffic signal control systems are deployed to accommodate real-time traffic conditions. Yet travel demand and behavior of the individual vehicles might be overseen by their model-based control algorithms and aggregated input data. Recent development of artificial intelligence, especially the success of deep learning, makes it possible to utilize information of individual vehicles to control the traffic signals. Several pioneering studies developed modelfree control algorithms using deep reinforcement learning. However, those studies are limited to isolated intersections and their effectiveness was only evaluated in ideal simulated traffic conditions by hypothetical benchmarks. To fill the gap, this study proposes a network-level decentralized adaptive signal control algorithmusing one of the famous deep reinforcement methods, double dueling deep Q network in the multi-agent reinforcement learning framework. The proposed algorithm was evaluated by the real-world coordinated actuated signals in a simulated suburban traffic corridor which emulates the real-field traffic condition. The evaluation results showed that the proposed deepreinforcement- learning-based algorithm outperforms the benchmark. It is able to reduce 10.27% of the travel time and 46.46% of the total delay.
Keywords: Deep reinforcement learning | Multi-agent reinforcement learning | Adaptive signal control
Deep reinforcement learning-based controller for path following of an unmanned surface vehicle
کنترلر مبتنی بر یادگیری تقویتی عمیق برای پیگیری مسیر یک وسیله نقلیه سطحی بدون سرنشین-2019
In this paper, a deep reinforcement learning (DRL)-based controller for path following of an unmanned surface vehicle (USV) is proposed. The proposed controller can self-develop a vehicle’s path following capability by interacting with the nearby environment. A deep deterministic policy gradient (DDPG) algorithm, which is an actor-critic-based reinforcement learning algorithm, was adapted to capture the USV’s experience during the path-following trials. A Markov decision process model, which includes the state, action, and reward formulation, specially designed for the USV path-following problem is suggested. The control policy was trained with repeated trials of path-following simulation. The proposed method’s path-following and self-learning capabilities were validated through USV simulation and a free-running test of the full-scale USV.
Keywords: Deep reinforcement learning | Path following | Unmanned surface vehicle | Learning-based control | Artificial intelligence
Deep reinforcement learning with its application for lung cancer detection in medical Internet of Things
یادگیری تقویتی عمیق با کاربرد آن برای تشخیص سرطان ریه در اینترنت اشیاء پزشکی -2019
Recently, deep reinforcement learning has achieved great success by integrating deep learning models into reinforcement learning algorithms in various applications such as computer games and robots. Specially, it is promising for computer-aided diagnosis and treatment to combine deep reinforcement learning with medical big data generated and collected from medical Internet of Things. In this paper, we focus on the potential of the deep reinforcement learning for lung cancer detection as many people are suffering from the lung tumor and about 1.8 million patients died from lung cancer in 2018. Early detection and diagnosis of lung tumor can significantly improve the treatment effect and prolong survival. In this work, we present several representative deep reinforcement learning models that are potential to use for lung cancer detection. Furthermore, we summarize the common types of lung cancer and the main characteristics of each type. Finally, we point out the open challenges and possible future research directions of applying deep reinforcement learning to lung cancer detection, which is expected to promote the evolution of smart medicine with medical Internet of Things.
Keywords: Smart medicine | Medical Internet of Things | Deep reinforcement learning | Lung cancer
A smart agriculture IoT system based on deep reinforcement learning
سیستم اینترنت اشیا کشاورزی هوشمند مبتنی بر یادگیری تقویت عمیق-2019
Smart agriculture systems based on Internet of Things are the most promising to increase food production and reduce the consumption of resources like fresh water. In this study, we present a smart agriculture IoT system based on deep reinforcement learning which includes four layers, namely agricultural data collection layer, edge computing layer, agricultural data transmission layer, and cloud computing layer. The presented system integrates some advanced information techniques, especially artificial intelligence and cloud computing, with agricultural production to increase food production. Specially, the most advanced artificial intelligence model, deep reinforcement learning is combined in the cloud layer to make immediate smart decisions such as determining the amount of water needed to be irrigated for improving crop growth environment. We present several representative deep reinforcement learning models with their broad applications. Finally, we talk about the open challenges and the potential applications of deep reinforcement learning in smart agriculture IoT systems.
Keywords: Deep reinforcement learning | Smart agriculture IoT | Edge computing | Cloud computing
Evaluating smart grid renewable energy accommodation capability with uncertain generation using deep reinforcement learning
ارزیابی قابلیت اسکان انرژی تجدیدپذیر شبکه هوشمند با تولید نامعین با استفاده از یادگیری تقویتی عمیق-2019
Due to environment-friendliness, renewable energy like solar power and wind power is more and more introduced to energy systems all over the world. Simultaneously, high penetrations of wind and solar generation also have brought severe curtailment of wind and solar. How to alleviate curtailment of wind and solar is a crucial problem in evaluating accommodation capability of renewable energy, which reflects the extent of utilization of renewable energy and economic benefits. The uncertainty of renewable energy brings challenges to precisely describe renewable generation, which leads to difficulty in designing effective mechanisms for accommodation capability of renewable energy. Existing work suffers from high computation overhead from frequently updated data, and low precision of describing renewable energy, which leads to less effective policies for renewable energy accommodation and underestimated accommodation capability. To make the most of renewable energy, an algorithm AccCap-DRL based on deep reinforcement learning is proposed. AccCap-DRL partitions a distribution into segments by time intervals, employs WGAN to describe distributions of renewable energy data, and employs DDPG to obtain approximate policies for renewable energy accommodation in different scenarios. Simulation results from real power generation and users’ demand data show high effectiveness of the proposed algorithm, and high efficiency of evaluating accommodation capability
Keywords: Accommodation capability | Deep reinforcement learning | Uncertain renewable energy description
Deep learning for decision making and the optimization of socially responsible investments and portfolio
یادگیری عمیق برای تصمیم گیری و بهینه سازی سرمایه گذاری ها و نمونه کارها با مسئولیت اجتماعی-2019
A socially responsible investment portfolio takes into consideration the environmental, social and governance aspects of companies. It has become an emerging topic for both financial investors and researchers recently. Traditional investment and portfolio theories, which are used for the optimization of financial investment portfolios, are inadequate for decision-making and the construction of an optimized socially responsible investment portfolio. In response to this problem, we introduced a Deep Responsible Investment Portfolio (DRIP) model that contains a Multivariate Bidirectional Long Short-Term Memory neural network, to predict stock returns for the construction of a socially responsible investment portfolio. The deep reinforcement learning technique was adapted to retrain neural networks and rebalance the portfolio periodically. Our empirical data revealed that the DRIP framework could achieve competitive financial performance and better social impact compared to traditional portfolio models, sustainable indexes and funds.
Keywords: Socially responsible investment | Portfolio optimization | Multivariate analytics | Deep reinforcement learning | Decision support systems
Energy management of hybrid electric bus based on deep reinforcement learning in continuous state and action space
مدیریت انرژی اتوبوس برقی هیبریدی مبتنی بر یادگیری تقویت عمیق در فضای پیوسته و فضای عملی-2019
Energy management is a fundamental task in hybrid electric vehicle community. Efficient energy management of hybrid electric vehicle is challenging owning to its enormous search space, multitudinous control variables and complicated driving conditions. Most existing methods apply discretization to approximate the continuous optimum in real driving conditions, which results in relatively low performance with the discretization error and curse of dimensionality. We introduce a novel energy management strategy with a deep reinforcement learning framework Actor-Critic to address these challenges. Actor-Critic uses a deep neural network, named as actor network, to directly output continuous control signals. Another deep neural network, named as critic network, evaluates the control signals generated by the actor network.The actor and critic neural network are trained by reinforcement learning from self-play in a continuous action space. Several comprehensive experiments are conducted in this paper, the proposed method surpasses discretization-based strategies by directly optimizing in the continuous space, which improves energy management performance while blackucing computation load. The simulation results indicate that the AC achieve the optimal energy distribution in comparison with the discretization-based strategies, especially surpassing the existing baseline DP by 5.5%, 2.9%, 9.5% in CTUDC, WVUCITY and WVUSUB in one-tenth of the computational cost.
Keywords: Self-learning energy management | Hybrid electric bus | Deep reinforcement learning | Continuous spaces