Deep reinforcement learning based planning method in state space for lunar rovers

Attempts to address the production scheduling problem thus far rely on simplifying assumptions, such as static environment and inflexible size of the problem, which compromises the schedule performance in practice due to many unpredictable disruptions to the system. Thus, the study of scheduling in the presence of real-time events, termed dynamic scheduling, continues to attract attention given the agility, flexibility, and timeliness modern production systems must deliver. Additionally, the changing nature of the manufacturing system also raises new challenges to existing scheduling strategies. At the front-end, the development of advanced data creation and exchange frameworks such as the Internet of things and cyber-physical system and their applications to the industrial environment have created an abundance of industrial data, while at the backend, edge and cloud computing technologies greatly enhance the capacity to process that data. Industrial data must be mined and analyzed so that the investment in infrastructure is not wasted, and the production system managed more effectively and in real-time. Many data-driven technologies have been adopted in scheduling research, a promising candidate among them being reinforcement learning (RL) which is able to build a direct mapping from observation of environment to actions that improve its performance. In this thesis, a deep multi-agent reinforcement learning (deep MARL) architecture is proposed to solve the dynamic scheduling problem (DSP). The deep reinforcement learning (DRL) algorithm is used to train the decentralized scheduling agents, to capture the relationship between information on the factory floor and scheduling objectives, with the aim of making real-time decisions for a manufacturing system with frequent unexpected events. Two major aspects of deep MARL application to DSP are addressed in this work, namely the conversion from traditional static scheduling problem (SSP) to dynamic scheduling in a practical context, and the adaptation of existing deep MARL algorithms to solve the scheduling problem in such an environment. Some impractical constraints of traditional studies are removed to create a research context that is closer to actual practice, result in a scheduling problem of variable size and scope. Specialized state and action representations that can handle the ever-changing specification of problem are developed; the criteria of feature selection in dynamic environment are also discussed. Recent progressions in DRL and MARL research are integrated into the proposed approach after selection and adaptation. In addition, various improvements to common deep MARL architecture are proposed, including the lightweight multilayer perceptron (MLP) encoder that is efficient in handling unstructured industrial data, a training scheme under the multi-agent architecture to improve the stability of training and overall performance, and knowledge-based reward-shaping techniques to decompose the joint reward signal into individual utilities to speed up the learning and encourage cooperative behavior between agents. Simulation studies are then conducted for the ablation study and validation. In the first stage, the performance of the proposed approach, either as individual components or as an integrated model, are tested in iterative simulation runs within which a unique instance of production is created. Meanwhile, a set of DRL-based approaches from recent publications are run in parallel. Results suggest that the contribution of each improvement is significant; the integrated architecture also delivers stronger performance than peer DRL-based approaches. For the validation, a set of priority rules that have strong performance in specified context and are widely applied in actual production scheduling are used as the benchmark. Proposed approach also provides performance gain compared to the strongest rule, with a minor increase in computation cost and negligible latency in decision-making.

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.

Traffic signal control is a complex problem and it is difficult to determine an optimal strategy to control multi-directional traffic at multiple intersections. Recent years have witnessed numerous successes of deep learning neural networks in the fields of artificial intelligence. Motivated by the dominant performance of neural networks, this study attempts to develop a novel adaptive signal control approach by fusing deep learning (DL) and reinforcement learning (RL), i.e., deep reinforcement learning (DRL), for arterial signal coordination. DRL can considerably improve the ability to deal with large amounts of data processing, systematic perception and expression, which is key to coordinated control of arterial intersections. The proposed algorithm is implemented by utilizing real-time traffic detection data and aims to optimize the hybrid global and local reward functions. The experimental results obtained by traffic simulation software SUMO demonstrate the advantage of the proposed approach, as well as its efficiency and effectiveness compared with fixed-time and actual signal control methods.

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.

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.

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.

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.

This paper discusses an autonomous driving control strategy based on Deep Reinforcement Learning (DRL), which aims to improve the decision-making ability of autonomous driving system in complex traffic environments. Deep reinforcement learning has a wide range of applications in many fields, such as robotics and medicine. Autonomous driving has emerged as a significant research focus in recent years. By combining deep learning and reinforcement learning, the model is able to autonomously learn and optimize driving behavior under dynamically changing road conditions. The DRL-based control strategy performs well in vehicle obstacle avoidance, pedestrian recognition, and traffic rule compliance in the face of complex environments such as city streets, intersections, and congested road sections, significantly improving the safety and efficiency of autonomous driving. This article will first introduce deep reinforcement learning. Then, the autonomous driving control strategy based on deep reinforcement learning is introduced. This research provides valuable insights for developing and implementing DRL-based autonomous driving systems.

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.

In recent years, deep reinforcement learning (DRL) has made impressive achievements in many fields. However, existing DRL algorithms usually require a large amount of exploration to obtain a good action policy. In addition, in many complex situations, the reward function cannot be well designed to meet task requirements. These two problems will make it difficult for DRL to learn a good action policy within a relatively short period. The use of expert data can provide effective guidance and avoid unnecessary exploration. This study proposes a deep imitation reinforcement learning (DIRL) algorithm that uses a certain amount of expert demonstration data to speed up the training of DRL. In the proposed method, the learning agent imitates the expert's action policy by learning from demonstration data. After imitation learning, DRL is used to optimise the action policy in a self‐learning way. By experimental comparison on a video game called the Mario racing game, it is shown that the proposed DIRL algorithm with expert demonstration data can obtain much better performance than previous DRL algorithms without expert guidance.

The traditional maneuver decision-making approaches are highly dependent on accurate and complete situation information, and their decision-making quality becomes poor when opponent information is occasionally missing in complex electromagnetic environments. In order to solve this problem, an autonomous maneuver decision-making approach is developed based on deep reinforcement learning (DRL) architecture. Meanwhile, a Transformer network is integrated into the actor and critic networks, which can find the potential dependency relationships among the time series trajectory data. By using these relationships, the information loss is partially compensated, which leads to maneuvering decisions being more accurate. The issues of limited experience samples, low sampling efficiency, and poor stability in the agent training state appear when the Transformer network is introduced into DRL. To address these issues, the measures of designing an effective decision-making reward, a prioritized sampling method, and a dynamic learning rate adjustment mechanism are proposed. Numerous simulation results show that the proposed approach outperforms the traditional DRL algorithms, with a higher win rate in the case of opponent information loss.

Irrigation decision-making using Reinforcement Learning (RL) performs well in changing environment, but easily falls into sub-optimal solutions with high-dimensional data. Deep Reinforcement Learning (DRL) has fused RL with Deep Learning (DL) and excels at learning adaptive and long-term irrigation strategies directly from high-dimensional environment data. This paper systematically reviews the applications of DRL in irrigation optimization, covering both pre-trained environments based on crop growth simulators and dynamic environments driven by real-time sensors. We discussed the strengths of classic DRL algorithms, including their ability to handle dynamic and non-linear environments, and reviewed their performance in irrigation multi-objective optimization and decision-making. In addition, we identified constraints in applying DRL in irrigation decision making, which include data scarcity, poor model interpretability, and difficulties in field deployment. It shows DRL can provide a powerful framework for adaptive irrigation, but is constrained by the gap between simulation and real-world complexity. To address these limitations, we discussed approaches in future work, such as developing multi-objective DRL algorithms. These approaches will improve DRL modeling outcomes and provide a technological foundation for smart agriculture and sustainable resource management. • Review the application of Deep Reinforcement Learning (DRL) in agricultural irrigation. • Analyze the performance of DRL algorithms in irrigation decision-making. • Compare DRL models based on different environment in irrigation optimization. • Discuss the further work to improve DRL performance in irrigation optimization.

A new ensemble deep graph reinforcement learning network for spatio-temporal traffic volume forecasting in a freeway network

The growing number of complex and heterogeneous Internet of Things (IoT) applications has imposed a high demand for scarce communications and computing resources. To meet this stringent requirement, it is desirable to develop large-scale highly adaptive online resource allocation strategies to streamline existing network operations. Deep reinforcement learning (DRL), which combines the merits of reinforcement learning and deep learning, is capable of addressing complex decision-making tasks, thus enabling efficient online resource allocation. In this article, we present a DRL-based resource allocation framework. We begin a discussion on DRL basics and review its several recent applications. Then, we develop two new DRL algorithms that facilitate unlocking the potential of DRL and offer viable solutions to many more complex resource allocation problems. The first one tackles an optimization problem exposed to mixed (discrete and continuous) action spaces and bound by a number of highly non-linear quality-of-service (QoS) constraints. The second one extends the single-agent DRL to a more challenging multi-agent DRL by introducing a novel semi-distributed architecture. Finally, we discuss the challenges and future visions of applying DRL to real-world IoT networks.

The International Lunar Research Station, to be established around 2030, will equip lunar rovers with robotic arms as constructors. Construction requires lunar soil and lunar rovers, for which rovers must go toward different waypoints without encountering obstacles in a limited time due to the short day, especially near the south pole. Traditional planning methods, such as uploading instructions from the ground, can hardly handle many rovers moving on the moon simultaneously with high efficiency. Therefore, we propose a new collaborative path-planning method based on deep reinforcement learning, where the heuristics are demonstrated by both the target and the obstacles in the artificial potential field. Environments have been randomly generated where small and large obstacles and different waypoints are created to collect resources, train the deep reinforcement learning agent to propose actions, and lead the rovers to move without obstacles, finish rovers’ tasks, and reach different targets. The artificial potential field created by obstacles and other rovers in every step affects the action choice of the rover. Information from the artificial potential field would be transformed into rewards in deep reinforcement learning that helps keep distance and safety. Experiments demonstrate that our method can guide rovers moving more safely without turning into nearby large obstacles or collision with other rovers as well as consuming less energy compared with the multi-agent A-Star path-planning algorithm with improved obstacle avoidance method.