Break through the limits of learning by machines

Reinforcement learning is a mathematical framework for agents to interact intelligently with their environment. Unlike supervised learning, where a system learns with the help of labeled data, reinforcement learning agents learn how to act by trial and error only receiving a reward signal from their environments. A field where reinforcement learning has been prominently successful is robotics [3]. However, real-world control problems are also particularly challenging because of the noise and high- dimensionality of input data (e.g., visual input). In recent years, in the field of supervised learning, deep neural networks have been successfully used to extract meaning from this kind of data. Building on these advances, deep reinforcement learning was used to solve complex problems like Atari games and Go. Mnih et al. [1] built a system with fixed hyper parameters able to learn to play 49 different Atari games only from raw pixel inputs. However, in order to apply the same methods to real-world control problems, deep reinforcement learning has to be able to deal with continuous action spaces. Discretizing continuous action spaces would scale poorly, since the number of discrete actions grows exponentially with the dimensionality of the action. Furthermore, having a parametrized policy can be advantageous because it can generalize in the action space. Therefore with this thesis we study state-of-the-art deep reinforcement learning algorithm, Deep Deterministic Policy Gradients. We provide a theoretical comparison to other popular methods, an evaluation of its performance, identify its limitations and investigate future directions of research. The remainder of the thesis is organized as follows. We start by introducing the field of interest, machine learning, focusing our attention of deep learning and reinforcement learning. We continue by describing in details the two main algorithms, core of this study, namely Deep Q-Network (DQN) and Deep Deterministic Policy Gradients (DDPG). We then provide implementatory details of DDPG and our test environment, followed by a description of benchmark test cases. Finally, we discuss the results of our evaluation, identifying limitations of the current approach and proposing future avenues of research.

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.

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.

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.

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.

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.

Forecasting the behavior of the stock market is a classic but difficult topic, one that has attracted the interest of both economists and computer scientists. Over the course of the last couple of decades, researchers have investigated linear models as well as models that are based on machine learning (ML), deep learning (DL), reinforcement learning (RL), and deep reinforcement learning (DRL) in order to create an accurate predictive model. Machine learning algorithms can now extract high-level financial market data patterns. Investors are using deep learning models to anticipate and evaluate stock and foreign exchange markets due to the advantage of artificial intelligence. Recent years have seen a proliferation of the deep reinforcement learning algorithm’s application in algorithmic trading. DRL agents, which combine price prediction and trading signal production, have been used to construct several completely automated trading systems or strategies. Our objective is to enable interested researchers to stay current and easily imitate earlier findings. In this paper, we have worked to explain the utility of Machine Learning, Deep Learning, Reinforcement Learning, and Deep Reinforcement Learning in Quantitative Finance (QF) and the Stock Market. We also outline potential future study paths in this area based on the overview that was presented before.

Deep reinforcement learning (DRL) has superior autonomous decision-making capabilities, combining deep learning and reinforcement learning (RL). Unlike DRL employs deep neural networks (DNNs), broad RL (BRL) adopts the broad learning system (BLS) that is established with flat networks to generate the strategy. This article proposes the multiagent adaptive broad-DRL (ABDRL) approach for traffic light control (TLC), which combines the broad network with the deep network structure. Specifically, the structure of ABDRL first expands in the form of flatted broad networks. Then, the feature representation module that contains DNNs is employed to extract the critical traffic information. In addition, experiences sampled randomly by the experience replay mechanism cannot reflect the current training status of the agent effectively. In order to alleviate the impacts caused by random sampling, the forgetful experience mechanism (FEM) is incorporated into ABDRL. The FEM enables the agent to discriminate the importance of experiences stored in the experience reply buffer to improve robustness and adaptability. We validate the effectiveness of ABDRL in TLC, and the results illustrate the optimality and robustness of ABDRL over the state-of-the-art multiagent DRL (MADRL) algorithms.

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.

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

In recent years, deep reinforcement learning has combined the advantages of reinforcement learning and deep learning, and has made great progress in decision-making tasks. However, the training of deep reinforcement learning requires frequent interactions between the agent and the environment and repeated experiments. Adversaries have chances to poison the sample data collected by the agent by attacking the experimental environment in the training process, thereby bringing security risks and serious consequences to the training process of reinforcement learning. This work is committed to addressing the security risks in the field of deep reinforcement learning. However, this work improves the algorithm from the perspective of sample data filtering, and improves the security performance of deep reinforcement learning algorithm. There are two contributions in this work: one is to defend against adversarial attacks against deep reinforcement learning through cluster analysis and sample value evaluation; the other is to propose a deep reinforcement learning algorithm based on sample value evaluation on the basis of deterministic strategy gradient algorithm. The algorithm uses the clustering method to classify the sample pool, and measures the contribution value and security risk of the sample to the model training through the sample value evaluation. The classic game experiments show that the proposed algorithm is safe and effective. It reduces the threat of the agent falling into the adversarial sample attack and improves the training performance of deep reinforcement learning.

In the domain of continuous control, deep reinforcement learning (DRL) demonstrates promising results. However, the dependence of DRL on deep neural networks (DNNs) results in the demand for extensive data and increased computational cost. To address this issue, a novel hybrid actor-critic reinforcement learning (RL) framework is introduced. The proposed framework integrates the broad learning system (BLS) with DNN, aiming to merge the strengths of both distinct architectural paradigms. Specifically, the critic network employs BLS for rapid value estimation via ridge regression, while the actor network retains the DNN structure to optimize policy gradients. This hybrid design is generalizable and can enhance existing actor-critic algorithms. To demonstrate its versatility, the proposed framework is integrated into three widely used actor-critic algorithms-deep deterministic policy gradient (DDPG), soft actor-critic (SAC), and twin delayed DDPG (TD3), resulting in BLS-augmented variants. The experimental results reveal that all BLS-enhanced versions surpass their original counterparts in terms of training efficiency and accuracy. These improvements highlight the suitability of the proposed framework for real-time control scenarios, where computational efficiency and rapid adaptation are critical.

Introduction: Deep reinforcement learning (deep RL) integrates the principles of reinforcement learning with deep neural networks, enabling agents to excel in diverse tasks ranging from playing board games such as Go and Chess to controlling robotic systems and autonomous vehicles. By leveraging foundational concepts of value functions, policy optimization, and temporal difference methods, deep RL has rapidly evolved and found applications in areas such as gaming, robotics, finance, and healthcare. Objective: This paper seeks to provide a comprehensive yet accessible overview of the evolution of deep RL and its leading algorithms. It aims to serve both as an introduction for newcomers to the field and as a practical guide for those seeking to select the most appropriate methods for specific problem domains. Methods: We begin by outlining fundamental reinforcement learning principles, followed by an exploration of early tabular Q-learning methods. We then trace the historical development of deep RL, highlighting key milestones such as the advent of deep Q-networks (DQN). The survey extends to policy gradient methods, actor–critic architectures, and state-of-the-art algorithms such as proximal policy optimization, soft actor–critic, and emerging model-based approaches. Throughout, we discuss the current challenges facing deep RL, including issues of sample efficiency, interpretability, and safety, as well as open research questions involving large-scale training, hierarchical architectures, and multi-task learning. Results: Our analysis demonstrates how critical breakthroughs have driven deep RL into increasingly complex application domains. We highlight existing limitations and ongoing bottlenecks, such as high data requirements and the need for more transparent, ethically aligned systems. Finally, we survey potential future directions, highlighting the importance of reliability and ethical considerations for real-world deployments.

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.