Stochastic inversion of magnetotelluric data using deep reinforcement learning

To solve problems involving sequential decision-making, Deep Reinforcement Learning (DRL) combines the advantages of Reinforcement Learning (RL) and Deep Learning (DL).These fusions are skilled at training agents for video games because they use neural networks to approximate nonlinear functions.The Deep Q Network (DQN), a key algorithm in this field, tends to overestimate Q-values despite its effectiveness.The Double Deep Q Network (Double DQN) was presented to address this.The Kera's framework in PyCharm is used in this study to examine the practical application and comparative analysis of DQN and Double DQN in the Flappy Bird game.To speed up its training, improvements were also made to the DQN model.The enhanced DQN performed better than the traditional DQN but less well than the Double DQN, according to the results.This study delves into the practical application and comparative analysis of DQN and Double DQN in the Flappy Bird game, utilizing the Kera's framework in PyCharm.Additionally, enhancements were made to the DQN model to expedite its training.Results affirmed that the enhanced DQN outperformed the conventional DQN but lagged the Double DQN.The training loss trajectory further substantiated Double DQN's superiority in mitigating overestimation issues.

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.

To decrease the impact of Partially Observable MDP on deep reinforcement learning performance of intersection signal control, A deep reinforcement learning is proposed in this paper with utilizing the real-time GPS data as well as learning the control of the traffic lights in single intersection. We integrate deep reinforcement learning network (DRQN) with recurrent neural network (RNN) and apply deep network, experience pool and greedy strategy in deep reinforcement learning strategy. It solves the problem of overestimation of target Q value and insufficient long-term experience learning in the standard reinforcement learning of traffic signal control. The comparison of performance was made between the proposed method and standard Deep Q-Network (DQN) on the partial observation of traffic situations. The experimental results show that both DQN and DRQN methods can adjust their traffic signal timing control strategies according to the specific traffic conditions as well as calculating a lower average delay time of vehicle than that of fixed-time control. Besides, the simulation effect of DRQN learning method is better than that of DQN learning method in different probe vehicle proportion environment.

The Mobile Reliable Opportunistic Routing (MROR) protocol improves data-forwarding reliability in Cognitive Radio Sensor Networks (CRSNs) through mobility-aware virtual contention groups and handover zoning. However, its heuristic decision logic is difficult to optimize under highly dynamic spectrum access and random node mobility. To address this limitation, we present DRL-MROR, a refined routing framework that incorporates deep reinforcement learning (DRL) to enable intelligent and adaptive forwarding decisions. In DRL-MROR, the secondary users (SUs) act as autonomous agents that observe local state information, including primary-user activity, link quality, residual energy, and neighbor-mobility patterns. Each agent learns a forwarding policy through a Deep Q-Network (DQN) optimized for long-term network utility in terms of throughput, delay, and energy efficiency. We formulate routing as a Markov Decision Process (MDP) and use experience replay with prioritized sampling to improve learning stability and convergence. The DQN used at each node is intentionally lightweight, requiring 5514 trainable parameters, about 21.5 kB of weight storage in 32-bit precision, and approximately 5.4k multiply-accumulate operations per inference, which supports practical deployment on edge-capable CRSN nodes. Extensive simulations show that DRL-MROR outperforms the original MROR protocol and representative AI-based routing baselines such as AIRoute under diverse operating conditions. The results indicate gains of up to 38% in throughput, 42% in goodput, a 29% reduction in energy consumed per packet, and an approximately 18% improvement in network lifetime, while maintaining high route stability and fairness. DRL-MROR also reduces control overhead by about 30% and average end-to-end delay by up to 32%, maintaining strong performance even under elevated PU activity and higher node mobility. These results show that augmenting opportunistic routing with lightweight DRL can substantially improve adaptability and efficiency in next-generation IoT-oriented CRSNs.

Deep Reinforcement Learning (RL) suffer from catastrophic forgetting due to being agnostic to the timescale of changes in the distribution of experiences. Although, RL algorithms are guaranteed to converge to optimal policies in Markov decision processes, this only holds in the presence of static environments. However, this assumption is very restrictive. In many real world problems like ridesharing, traffic control, etc., we are dealing with highly dynamic environments, where RL methods yield only sub-optimal decisions. In this paper, we introduce an adaptive model-free deep reinforcement approach that can recognize diurnal patterns in the ridesharing environment. To achieve this, we (1) adopt a change point detection algorithm to detect the changes in the distribution of experiences, then (2) we develop a Deep Q Network (DQN) agent that is capable of recognizing diurnal patterns and making informed dispatching decisions according to the changes in the underlying environment. Based on the demand, our DQN approach re-balances idle vehicles by dispatching them to the areas of anticipated high demand using Deep Reinforcement Learning. This approach can be adopted in various domains through tuning the RL agent's objective function, where it will still capture the changes in the corresponding underlying environment. Our framework is validated using the New York City Taxi public dataset. Experimental results show the effectiveness of our approach in real-time and large scale settings.

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.

In this paper, we propose a two-stage Deep Reinforcement Learning (DRL) framework for the automatic arrangement of buildings in urban planning, with a focus on satisfying critical regulatory constraints: sunshine duration and inter-building distance. To overcome the computational bottleneck of high-fidelity sunshine simulation during DRL training, we first introduce a Convolutional Auto-Encoder (CAE) model that serves as a fast and accurate surrogate for predicting sunshine distribution across a building layout. This CAE model, trained on data generated from Autodesk Revit, achieves near real-time prediction (∼0.015 s per layout) with an average error of less than 3 min, enabling its seamless integration into the iterative RL loop. Subsequently, we formulate the sequential placement of buildings as a Markov Decision Process (MDP) and employ a Deep Q-Network (DQN) to learn the optimal placement policy. The DQN agent interacts with a planning environment, where its actions (moving a building in discrete directions) are rewarded based on the satisfaction of distance constraints and the avoidance of low-sunshine zones identified by the CAE. Our integrated CAE-DQN system automates the generation of compliant building layouts. Experimental evaluation demonstrates that our method significantly outperforms baseline approaches (Random Placement, Greedy Heuristic, and Genetic Algorithm) in terms of success rate (100%), convergence speed, and layout compactness. The proposed framework not only ensures hard constraint satisfaction but also provides a practical and scalable tool for rapid prototype generation in the conceptual design phase of urban planning.

By addressing the drawbacks of static optimization techniques, this research seeks to improve the dynamic energy management of thermoelectric generators (TEGs). Finding the best deep reinforcement learning (DRL) algorithm to maximize energy distribution, prolong battery life, and boost system efficiency in the face of the variable conditions present in waste heat recovery systems is the goal. The TEG system was modeled using the Markov decision process and implemented in a computer-simulated environment. Three advanced DRL algorithms were used: soft actor-critic (SAC), proximal policy optimization (PPO), and deep Q-networks (DQN); which were trained to act as intelligent controllers. The performance of each algorithm was systematically evaluated and compared using key metrics, including average cumulative reward, battery health, system efficiency, and a novel metric termed the energy fulfillment rate, which measures the ability to meet demand while storing surplus energy. The comparative analysis revealed a critical trade-off between maximizing performance and ensuring hardware longevity. The SAC algorithm demonstrated the best overall performance, achieving the highest average reward (− 7.03) and energy fulfillment rate (22.84%). However, the A2C, DDPG, and PPO algorithms all achieved a perfect average battery health of 100.00%, highlighting their superior capability for preserving system longevity, albeit with slightly lower rewards. The DQN algorithm consistently showed the least effective performance across all metrics, particularly in maintaining battery health (60.73%). The SAC algorithm is the most suitable of the methods tested for dynamically managing TEG systems. Its underlying principle of maximization of entropy enables a better exploration of control strategies, leading to a better balance between immediate energy dispatch and long-term storage goals. The findings confirm the significant potential of DRL to create efficient and adaptive controllers for renewable energy applications, although further validation of physical hardware is required to confirm real-world viability.Supplementary InformationThe online version contains supplementary material available at 10.1038/s41598-025-27210-7.

Deep reinforcement learning for wireless sensor scheduling in cyber–physical systems

Choosing the right parameter configurations for recurring jobs running on big data analytics platforms is difficult because there can be hundreds of possible parameter configurations to pick from. Even the selection of parameter configurations is based on different types of applications and user requirements. The difference between the best configuration and the worst configuration can have a performance impact of more than 10 times. However, parameters of big data platforms are not independent, which makes it a challenge to automatically identify the optimal configuration for a broad spectrum of applications. To alleviate these problems, we proposed MonkeyKing, a system that leverages past experience and collects new information to adjust parameter configurations of big data platforms. It can recommend key parameters, which have strong impact on performance according to job types, and then combine deep reinforcement learning (DRL) to optimize key parameters to improve job performance. We choose the current popular deep Q-network (DQN) structure and its four improved algorithms, including DQN, Double DQN, Dueling DQN, and the combined Double DQN and Dueling DQN, and finally found that the combined Double DQN and Dueling DQN has a better effect. Our experiments and evaluations on Spark show that performance can be improved by ∼25% under best conditions.

Path planning in an unknown environment is a basic task for mobile robots to complete tasks. As a typical deep reinforcement learning, deep Q-network (DQN) algorithm has gained wide popularity in path planning tasks due to its self-learning and adaptability to complex environment. However, most of path planning algorithms based on DQN spend plenty of time for model training and the learned model policy depends only on the information observed by sensors. It will cause poor generalization capability for the new task and time waste for model retraining. Therefore, a new deep reinforcement learning method combining DQN with prior knowledge is proposed to reduce training time and enhance generalization capability. In this method, a fuzzy logic controller is designed to avoid the obstacles and help the robot avoid blind exploration for reducing the training time. A target-driven approach is used to address the lack of generalization, in which the learned policy depends on the fusion of observed information and target information. Extensive experiments show that the proposed algorithm converges faster than DQN algorithm in path planning tasks and the target can be reached without retraining when the path planning task changes.

Humans have to respond to everyday tasks with goal-directed actions in complex and time-continuous environments. However, modeling human behavior in such environments has been challenging. Deep Q-networks (DQNs), an application of deep learning used in reinforcement learning (RL), enable the investigation of how humans transform high-dimensional, time-continuous visual stimuli into appropriate motor responses. While recent advances in DQNs have led to significant performance improvements, it has remained unclear whether these advancements translate into improved modeling of human behavior. Here, we recorded motor responses in human participants (N = 23) while playing three distinct arcade games. We used stimulus features generated by a DQN as predictors for human data by fitting the DQN’s response probabilities to human motor responses using a linear model. We hypothesized that advancements in RL models would lead to better prediction of human motor responses. Therefore, we used features from two recently developed DQN models (Ape-X and SEED) and a third baseline DQN to compare prediction accuracy. Compared to the baseline DQN, Ape-X and SEED involved additional structures, such as dueling and double Q-learning, and a long short-term memory, which considerably improved their performances when playing arcade games. Since the experimental tasks were time-continuous, we also analyzed the effect of temporal resolution on prediction accuracy by smoothing the model and human data to varying degrees. We found that all three models predict human behavior significantly above chance level. SEED, the most complex model, outperformed the others in prediction accuracy of human behavior across all three games. These results suggest that advances in deep RL can improve our capability to model human behavior in complex, time-continuous experimental tasks at a fine-grained temporal scale, thereby opening an interesting avenue for future research that complements the conventional experimental approach, characterized by its trial structure and use of low-dimensional stimuli.

In this article, we consider a subclass of partially observable Markov decision process (POMDP) problems which we termed confounding POMDPs. In these types of POMDPs, temporal difference (TD)-based reinforcement learning (RL) algorithms struggle, as TD error cannot be easily derived from observations. We solve these types of problems using a new bio-inspired neural architecture that combines a modulated Hebbian network (MOHN) with deep Q-network (DQN), which we call modulated Hebbian plus Q-network architecture (MOHQA). The key idea is to use a Hebbian network with rarely correlated bio-inspired neural traces to bridge temporal delays between actions and rewards when confounding observations and sparse rewards result in inaccurate TD errors. In MOHQA, DQN learns low-level features and control, while the MOHN contributes to high-level decisions by associating rewards with past states and actions. Thus, the proposed architecture combines two modules with significantly different learning algorithms, a Hebbian associative network and a classical DQN pipeline, exploiting the advantages of both. Simulations on a set of POMDPs and on the Malmo environment show that the proposed algorithm improved DQN's results and even outperformed control tests with advantage-actor critic (A2C), quantile regression DQN with long short-term memory (QRDQN + LSTM), Monte Carlo policy gradient (REINFORCE), and aggregated memory for reinforcement learning (AMRL) algorithms on most difficult POMDPs with confounding stimuli and sparse rewards.

Software defined network (SDN) is a promising technology which can reduce network management complexity through the decoupling of the control plane and data plane. Due to large number of switches in the data plane, distributed and multiple controllers are necessary in the control plane for managing the switches. The switch controller mapping strategy for identifying the mapping relationships between the switch and controller is crucial in order to optimize the network performance. Considering the dynamics of the network behavior, it is quite important and challenging to develop models to reflect the network topology dynamics and to propose method for solving the long-term network performance optimization. Inspired by the recent advances in Artificial Intelligence (AI), in this paper, we propose a Deep Reinforcement Learning (DRL) based strategy for solving the switch controller mapping problem. A DRL based mapping strategy is proposed, in which Markov Decision Process (MDP) formulation is devised and Deep $Q$ -network (DQN) is proposed to achieve the maximization of long-term system performance by leveraging network latency, load balancing and system stability. Extensive simulations show that the DQN based algorithm can achieve the best system stability results while maintaining moderate switch controller latency and system equilibrium performance comparing with the optimization which only considers current system performance for switch controller mapping decision, and the optimization approaches which generate mapping decisions purely based on latency or load balancing separately.

With the rapid development of AI technology, personalized learning path planning is becoming an important research field. Dual system teaching models have great influence on the learning effect of students. Therefore, how to optimize the learning process of students through the personalized learning path is a great challenge. A Deep Reinforcement Learning (DRL) algorithm for personalized learning path planning is proposed. Based on deep Q-networks and reinforcement learning feedback mechanisms, the algorithm achieves adaptive optimization. The experimental results show that the algorithm based on the DRL is superior to the traditional method in learning performance, learning efficiency and satisfaction. Compared with traditional path planning algorithms based on rules and collaborative filtering, the proposed algorithm improves learning performance by 20%. Moreover, it has obvious advantages in learning motivation and efficiency. This paper provides a new way of thinking to intelligent education system, especially dual-system teaching model, and it is of great practical significance.