A Wood Quality Defect Detection System Based on Deep Learning and Multicriterion Framework

The design and implementation of a deep reinforcement learning and quantum finance theory-inspired portfolio investment management system

A novel data-driven energy management strategy for fuel cell hybrid electric bus based on improved twin delayed deep deterministic policy gradient algorithm

Anti-rolling devices are widely used in modern shipboard components. In particular, ship anti-rolling control systems are developed to achieve a wide range of ship speeds and efficient anti-rolling capabilities. However, factors that are challenging to solve accurately, such as strong nonlinearities, a complex working environment, and hydrodynamic system parameters, limit the investigation of the rolling motion of ships at sea. Moreover, current anti-rolling control systems still face several challenges, such as poor nonlinear adaptability and manual parameter adjustment. In this regard, this study developed a dynamic model for a ship anti-rolling system. In addition, based on deep reinforcement learning (DRL), an efficient anti-rolling controller was developed using a deep deterministic policy gradient (DDPG) algorithm. Finally, the developed system was applied to a ship anti-rolling device based on the Magnus effect. The advantages of reinforcement learning adaptive control enable controlling an anti-rolling system under various wave angles, ship speeds, and wavelengths. The results revealed that the anti-rolling efficiency of the intelligent ship anti-rolling control method using the DDPG algorithm surpassed 95% and had fast convergence. This study lays the foundation for developing a DRL anti-rolling controller for full-scale ships.

This work proposes a non-singular fast terminal sliding mode control (NFTSMC) strategy based on the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm and a nonlinear disturbance observer (NDO) to address the issues of modeling errors, motion disturbances, and transmission friction in robotic manipulators. Firstly, a novel modular serial 5-DOF robotic manipulator configuration is designed, and its kinematic and dynamic models are established. Secondly, a nonlinear disturbance observer is employed to estimate the total disturbance of the system and apply feedforward compensation. Based on boundary layer technology, an improved NFTSMC method is proposed to accelerate the convergence of tracking errors, reduce chattering, and avoid singularity issues inherent in traditional terminal sliding mode control. The stability of the designed control system is proved using Lyapunov stability theory. Subsequently, a deep reinforcement learning (DRL) agent based on the TD3 algorithm is trained to adaptively adjust the control gains of the non-singular fast terminal sliding mode controller. The dynamic information of the robotic manipulator is used as the input to the TD3 agent, which searches for optimal controller parameters within a continuous action space. A composite reward function is designed to ensure the stable and efficient learning of the TD3 agent. Finally, the motion characteristics of three joints for the designed 5-DOF robotic manipulator are analyzed. The results show that compared to the non-singular fast terminal sliding mode control algorithm based on a nonlinear disturbance observer (NDONFT), the non-singular fast terminal sliding mode control algorithm integrating a nonlinear disturbance observer and the Twin Delayed Deep Deterministic Policy Gradient algorithm (TD3NDONFT) reduces the mean absolute error of position tracking for the three joints by 7.14%, 19.94%, and 6.14%, respectively, and reduces the mean absolute error of velocity tracking by 1.78%, 9.10%, and 2.11%, respectively. These results verify the effectiveness of the proposed algorithm in enhancing the trajectory tracking accuracy of the robotic manipulator under unknown time-varying disturbances and demonstrate its strong robustness against sudden disturbances.

The research on autonomous driving based on deep reinforcement learning algorithms is a research hotspot. Traditional autonomous driving requires human involvement, and the autonomous driving algorithms based on supervised learning must be trained in advance using human experience. To deal with autonomous driving problems, this paper proposes an improved end-to-end deep deterministic policy gradient (DDPG) algorithm based on the convolutional block attention mechanism, and it is called multi-input attention prioritized deep deterministic policy gradient algorithm (MAPDDPG). Both the actor network and the critic network of the model have the same structure with symmetry. Meanwhile, the attention mechanism is introduced to help the vehicles focus on useful environmental information. The experiments are conducted in the open racing car simulator (TORCS)and the results of five experiment runs on the test tracks are averaged to obtain the final result. Compared with the state-of-the-art algorithm, the maximum reward increases from 62,207 to 116,347, and the average speed increases from 135 km/h to 193 km/h, while the number of success episodes to complete a circle increases from 96 to 147. Also, the variance of the distance from the vehicle to the center of the road is compared, and the result indicates that the variance of the DDPG is 0.6 m while that of the MAPDDPG is only 0.2 m. The above results indicate that the proposed MAPDDPG achieves excellent performance.

A Self-driving car using an end-to-end deep reinforcement learning[1] algorithms trained on lane-keeping task performs well in circuits that don't need decision making but cannot deal with situations like choosing to turn left or right in an upcoming crossroads, deciding when to leave a traffic circle or toward which path/destination to go. In this paper we propose a new Deep Reinforcement Learning architecture that supports external command as high-level input, that we call Steered Deep Reinforcement Learning (SDRL), we apply the SDRL architecture on the Deep Deterministic Policy Gradient algorithm DDPG and use CARLA a High-fidelity realistic driving simulator as a testbed environment to train and experiment the new model, since testing in ground truth turns out to be costly and risky. The Steered DDPG (SDDPG) model performs well on the road/roundabouts and responds correctly to the external commands that allow the driving agent to take the right turns.

We report on an aberration correction algorithm for a wavefront sensorless adaptive optics (WFSless AO) system based on deep reinforcement learning. First, it is verified that the reinforcement learning theory can be applied in our system. In addition, the deep deterministic policy gradient algorithm is introduced to build the control structure. After that, deep learning is used to deal with the messy raw images of far-field intensity distribution. We emphatically present how to design a feature extraction with the convolutional neural network in the control structure. To demonstrate the performance of this structure, some comparisons are made with the stochastic parallel gradient descent algorithm and the WFSless AO based on general modes algorithm. The results indicate that the correction speed of our method improves about 9 times and 2.5 times, respectively, for the similar correction effect.

Design automation is undergoing a new generation of changes caused by artificial intelligence technologies represented by deep learning and reinforcement learning. Notably, the advantages of deep reinforcement learning in addressing solution optimisation and decision-making tasks with cognitive automation functionality have garnered attention in design. In the context of surrogate model-driven engineering design optimisation, this paper addresses current research challenges such as reliance on domain knowledge for local development, shortcomings in the self-learning and adaptive capabilities of optimisation algorithms for global exploration, etc. Centred around the deep reinforcement learning model, Deep Q-learning, and complemented by self-organising maps and neural network technologies, we propose a methodology considering multi-fidelity simulation data for design space exploration. This approach effectively reduces sampling costs and enables the optimisation model to learn the optimal direction for high-precision predictions and achieve rapid, accurate optimisation. Finally, the effectiveness of the proposed method is comprehensively validated through four typical optimisation scenarios and a case study involving the optimisation of a wheeled robot's suspension swing arm structure. This work will be a crucial reference for applying deep reinforcement learning in simulation-driven engineering design optimisation.

Path planning refers to that the mobile robot can obtain the surrounding environment information and its own state information through the sensor carried by itself, which can avoid obstacles and move towards the target point. Deep reinforcement learning consists of two parts: reinforcement learning and deep learning, mainly used to deal with perception and decision-making problems, has become an important research branch in the field of artificial intelligence. This paper first introduces the basic knowledge of deep learning and reinforcement learning. Then, the research status of deep reinforcement learning algorithm based on value function and strategy gradient in path planning is described, and the application research of deep reinforcement learning in computer game, video game and autonomous navigation is described. Finally, I made a brief summary and outlook on the algorithms and applications of deep reinforcement learning.

Learning ability is the basic characteristic of human intelligence. The July 1, 2005 issue of Science published a list of 125 important questions in science. Among them, the question 94 “What are the limits of learning by machines?”. The annotation “Computers can already beat the world’s best chess players, and they have a wealth of information on the Web to draw on. But abstract reasoning is still beyond any machine”. In recent artificial intelligence has made great progresses. In 1997, the rise of the man-machine war, IBM Supercomputer Deep Blue defeated the chess master Garry Kasparov. On February 14, 2011, IBM’s Watson supercomputer won a practice round against Jeopardy champions Ken Jennings and Brad Rutter. In March 2016, Google DeepMind’s AlphaGo sealed a 4-1 victory over a South Korean Go grandmaster Lee Se-dol. This paper focuses on the machine learning methods of AlphaGo, including reinforcement learning, deep learning, deep reinforcement learning, analysis of the existing problems and the latest research progress. Deep reinforcement learning is the combination of deep learning and reinforcement learning, which can realize the learning algorithm from the perception to action. Simply said, this is the same as human behavior, input sensing information such as vision, and then, direct output action through the deep neural network. Deep reinforcement learning has the potential to learn a variety of skills for the robot to achieve full autonomy. Even though reinforcement learning is practiced successfully, but feature states need to manually set, for complex scene is a difficult thing, especially easy to cause the dimension disaster, and expression is not good. In 2010, Sascha Lange and Martin Riedmiller proposed deep auto-encoder neural networks in reinforcement learning to extract feature, which is used to control the visual correlation. In 2013, DeepMind proposed deep Q-network (DQN) in NIPS 2013, using convolution neural network to extract features, and then applied in reinforcement learning. They continue to improve and published an improved version of DQN on Nature in 2015, which has aroused widespread concern. In order to break through the limits of learning by machines, cognitive machine learning is proposed, which is the combination of machine learning and brain cognition, so that the machine intelligence is constantly evolving, and gradually reaches the human level of artificial intelligence. A cognitive model entitled Consciousness And Memory (CAM) is proposed by author, which consists of memory, consciousness, high-level cognitive functions, perception and motor. High-level cognitive functions of the brain include learning, language, thinking, decision making, emotion, and so on. Learning is a course to accept the stimulus through the nervous system and obtain new behavior, habits and accumulation experience. According to the current research progress of brain science and cognitive science, cognitive machine learning may be interested in learning emergence, procedural memory knowledge learning, learning evolution and so on. For intelligence, so-called evolution is refers to the learning of learning and the structure also follows the change. It is important to record the learning result by structure changing and improve the learning method.

Battery health-aware and naturalistic data-driven energy management for hybrid electric bus based on TD3 deep reinforcement learning algorithm

A hybrid energy storage system (HESS) is adopted to tackle the traction network voltage fluctuation problem caused by high power and large energy demand during the starting and braking of urban rail trains. The system is composed of on-board ultracapacitors and ground lithium batteries, aiming to smooth out the power fluctuation to realize “peak-shaving and valley-filling”. Based on deep reinforcement learning (DRL) online sequence decision, a dynamic power allocation strategy is proposed to improve the energy-saving and voltage stabilization of DC traction networks as well as HESS life protection. Furthermore, to enhance the DRL’s efficiency under time-varying operating conditions, an annealing bias - priority experience replay twin delayed deep deterministic policy gradient algorithm (A-TD3) is proposed to train the replay buffer in DRL. The online learning and optimization strategy is implemented via the mechanism of “trial and error” and “feedback” of the agent. RT-LAB semi-physical real-time simulation systems are adopted to verify the effectiveness of the proposed strategy. Compared with the traditional filtering algorithms and DRL algorithms, the results show that the proposed method converges faster and is more energy saving and stable while effectively protecting the energy storage components.

Deep reinforcement learning optimized double exponentially weighted moving average controller for chemical mechanical polishing processes

With the improvement in the intelligence level of UAVs and the development of cluster control technology, the intelligent decision-making method of UAV cluster confrontation will become the key technology of UAV combat in the future. The UAV cluster confrontation learning environment is of complex characteristics of high dimension, non-linearity, incomplete information, continuous action space, and so on. Recently, artificial intelligence technology represented by deep learning and reinforcement learning has made a great breakthrough. Deep reinforcement learning has shown a great ability to solve intelligent decision-making problems in complex environments. In this paper, enlightened by the multi-agent centralized training distributed execution framework and the idea of maximum policy entropy, we propose a deep reinforcement learning method of the multi-agent soft actor-critic (MASAC) with incomplete information. A game model for UAV cluster confrontation based on multi-agent deep reinforcement learning is established, and a continuous space multiple UAV combat environment is constructed. Simulation experiments are performed on the asymmetric confrontation of UAV clusters with red and blue teams. The experimental results show that MASAC outperforms the existing popular multi-agent deep reinforcement learning methods, making the game players converge to a higher return equilibrium point of the game. Moreover, the convergence of MASAC is investigated and analyzed extensively, and the results show that MASAC is of good convergence and stability, ensuring the practicability of MASAC in intelligent decision-making regarding UAV cluster confrontation.

Deep reinforcement learning (DRL) is one of the emerging areas of machine learning which focuses on maximized rewards. DRL is a type of machine learning that combines reinforcement learning and deep learning. It uses a series of algorithms to enable an agent to learn how to make decisions in a complex environment. DRL is a subset of artificial intelligence that focuses on making decisions based on the environment and the rewards associated with each action.The goal of DRL is to maximize the long-term reward of an agent. In order to do this, the agent must use a combination of deep learning, reinforcement learning and other AI techniques to learn which actions will lead to the highest reward. DRL is used to solve a variety of problems, from playing video games to controlling robots. It is also used in autonomous driving and robotics, as well as for financial trading. DRL is a powerful tool for solving complex problems and has been used in a variety of research projects. DRL has the potential to revolutionize the way we interact with machines and the environment.