Search:
Match:
11 results
Research Paper#Reinforcement Learning, Control Theory, Stability🔬 ResearchAnalyzed: Jan 3, 2026 06:18

MSACL: Lyapunov-Certified RL for Stable Control

Published:Dec 31, 2025 16:36
1 min read
ArXiv

Analysis

This paper addresses the critical challenge of ensuring provable stability in model-free reinforcement learning, a significant hurdle in applying RL to real-world control problems. The introduction of MSACL, which combines exponential stability theory with maximum entropy RL, offers a novel approach to achieving this goal. The use of multi-step Lyapunov certificate learning and a stability-aware advantage function is particularly noteworthy. The paper's focus on off-policy learning and robustness to uncertainties further enhances its practical relevance. The promise of publicly available code and benchmarks increases the impact of this research.
Reference

“MSACL achieves exponential stability and rapid convergence under simple rewards, while exhibiting significant robustness to uncertainties and generalization to unseen trajectories.”

PermalinkArXiv
Research Paper#Wireless Communication, Reinforcement Learning, UAV, RIS🔬 ResearchAnalyzed: Jan 3, 2026 08:42

Throughput Optimization in UAV-Mounted RIS using DRL

Published:Dec 31, 2025 10:36
1 min read
ArXiv

Analysis

This paper addresses a practical problem in wireless communication: optimizing throughput in a UAV-mounted Reconfigurable Intelligent Surface (RIS) system, considering real-world impairments like UAV jitter and imperfect channel state information (CSI). The use of Deep Reinforcement Learning (DRL) is a key innovation, offering a model-free approach to solve a complex, stochastic, and non-convex optimization problem. The paper's significance lies in its potential to improve the performance of UAV-RIS systems in challenging environments, while also demonstrating the efficiency of DRL-based solutions compared to traditional optimization methods.
Reference

“The proposed DRL controllers achieve online inference times of 0.6 ms per decision versus roughly 370-550 ms for AO-WMMSE solvers.”

PermalinkArXiv
Research Paper#Robotics, Reinforcement Learning, Autonomous Navigation🔬 ResearchAnalyzed: Jan 3, 2026 17:16

DRL for UGV Navigation in Crowded Environments

Published:Dec 30, 2025 15:17
1 min read
ArXiv

Analysis

This paper addresses the limitations of existing DRL-based UGV navigation methods by incorporating temporal context and adaptive multi-modal fusion. The use of temporal graph attention and hierarchical fusion is a novel approach to improve performance in crowded environments. The real-world implementation adds significant value.
Reference

“DRL-TH outperforms existing methods in various crowded environments. We also implemented DRL-TH control policy on a real UGV and showed that it performed well in real world scenarios.”

PermalinkArXiv
Research Paper#Vehicle Routing, Deep Reinforcement Learning, Optimization🔬 ResearchAnalyzed: Jan 3, 2026 15:43

Deep RL for Fleet Size and Mix VRP

Published:Dec 30, 2025 14:26
1 min read
ArXiv

Analysis

This paper addresses the Fleet Size and Mix Vehicle Routing Problem (FSMVRP), a complex variant of the VRP, using deep reinforcement learning (DRL). The authors propose a novel policy network (FRIPN) that integrates fleet composition and routing decisions, aiming for near-optimal solutions quickly. The focus on computational efficiency and scalability, especially in large-scale and time-constrained scenarios, is a key contribution, making it relevant for real-world applications like vehicle rental and on-demand logistics. The use of specialized input embeddings for distinct decision objectives is also noteworthy.
Reference

“The method exhibits notable advantages in terms of computational efficiency and scalability, particularly in large-scale and time-constrained scenarios.”

PermalinkArXiv
Research#IoT, AI, Networking, URLLC, DRL, Bayesian Optimization🔬 ResearchAnalyzed: Jan 4, 2026 06:49

Joint Link Adaptation and Device Scheduling Approach for URLLC Industrial IoT Network: A DRL-based Method with Bayesian Optimization

Published:Dec 29, 2025 14:32
1 min read
ArXiv

Analysis

The article proposes a DRL-based method with Bayesian optimization for joint link adaptation and device scheduling in URLLC industrial IoT networks. This suggests a focus on optimizing network performance for ultra-reliable low-latency communication, a critical requirement for industrial applications. The use of DRL (Deep Reinforcement Learning) indicates an attempt to address the complex and dynamic nature of these networks, while Bayesian optimization likely aims to improve the efficiency of the learning process. The source being ArXiv suggests this is a research paper, likely detailing the methodology, results, and potential advantages of the proposed approach.
Reference

“The article likely details the methodology, results, and potential advantages of the proposed approach.”

PermalinkArXiv
Paper#llm🔬 ResearchAnalyzed: Jan 3, 2026 16:08

Splitwise: Adaptive Edge-Cloud LLM Inference with DRL

Published:Dec 29, 2025 08:57
1 min read
ArXiv

Analysis

This paper addresses the challenge of deploying large language models (LLMs) on edge devices, balancing latency, energy consumption, and accuracy. It proposes Splitwise, a novel framework using Lyapunov-assisted deep reinforcement learning (DRL) for dynamic partitioning of LLMs across edge and cloud resources. The approach is significant because it offers a more fine-grained and adaptive solution compared to static partitioning methods, especially in environments with fluctuating bandwidth. The use of Lyapunov optimization ensures queue stability and robustness, which is crucial for real-world deployments. The experimental results demonstrate substantial improvements in latency and energy efficiency.
Reference

“Splitwise reduces end-to-end latency by 1.4x-2.8x and cuts energy consumption by up to 41% compared with existing partitioners.”

PermalinkArXiv
Research Paper#Control Systems, Reinforcement Learning, Nonlinear Systems🔬 ResearchAnalyzed: Jan 3, 2026 19:46

IRL-Based SDRE for Nonlinear Control

Published:Dec 27, 2025 18:03
1 min read
ArXiv

Analysis

This paper presents a novel approach to control nonlinear systems using Integral Reinforcement Learning (IRL) to solve the State-Dependent Riccati Equation (SDRE). The key contribution is a partially model-free method that avoids the need for explicit knowledge of the system's drift dynamics, a common requirement in traditional SDRE methods. This is significant because it allows for control design in scenarios where a complete system model is unavailable or difficult to obtain. The paper demonstrates the effectiveness of the proposed approach through simulations, showing comparable performance to the classical SDRE method.
Reference

“The IRL-based approach achieves approximately the same performance as the conventional SDRE method, demonstrating its capability as a reliable alternative for nonlinear system control that does not require an explicit environmental model.”

PermalinkArXiv
Research Paper#Image Super-Resolution, Reinforcement Learning, Reward Models🔬 ResearchAnalyzed: Jan 3, 2026 16:23

FinPercep-RM: Fine-grained Reward Model for Real-world Super-Resolution

Published:Dec 27, 2025 16:55
1 min read
ArXiv

Analysis

This paper addresses the limitations of traditional Image Quality Assessment (IQA) models in Reinforcement Learning for Image Super-Resolution (ISR). By introducing a Fine-grained Perceptual Reward Model (FinPercep-RM) and a Co-evolutionary Curriculum Learning (CCL) mechanism, the authors aim to improve perceptual quality and training stability, mitigating reward hacking. The use of a new dataset (FGR-30k) for training the reward model is also a key contribution.
Reference

“The FinPercep-RM model provides a global quality score and a Perceptual Degradation Map that spatially localizes and quantifies local defects.”

PermalinkArXiv
Research#LLM🔬 ResearchAnalyzed: Jan 10, 2026 10:53

RADAR: Novel RL-Based Approach Speeds LLM Inference

Published:Dec 16, 2025 04:13
1 min read
ArXiv

Analysis

This ArXiv paper introduces RADAR, a novel method leveraging Reinforcement Learning to accelerate inference in Large Language Models. The dynamic draft trees offer a promising avenue for improving efficiency in LLM deployments.
Reference

“The paper focuses on accelerating Large Language Model inference.”

PermalinkArXiv
Research#llm🔬 ResearchAnalyzed: Jan 4, 2026 10:31

MIND-V: Hierarchical Video Generation for Long-Horizon Robotic Manipulation with RL-based Physical Alignment

Published:Dec 7, 2025 02:28
1 min read
ArXiv

Analysis

This article introduces MIND-V, a novel approach for generating videos to facilitate long-horizon robotic manipulation. The core of the method lies in hierarchical video generation and reinforcement learning (RL) for physical alignment. The use of RL suggests an attempt to learn optimal control policies for the robot, while the hierarchical approach likely aims to decompose complex tasks into simpler, manageable sub-goals. The focus on physical alignment indicates a concern for the realism and accuracy of the generated videos in relation to the physical world.
Reference

“”

PermalinkArXiv
Research#llm📝 BlogAnalyzed: Dec 29, 2025 07:28

AI Trends 2024: Reinforcement Learning and LLMs with Kamyar Azizzadenesheli

Published:Feb 5, 2024 19:14
1 min read
Practical AI

Analysis

This article from Practical AI discusses the intersection of Reinforcement Learning (RL) and Large Language Models (LLMs) in the context of AI trends for 2024. It features an interview with Kamyar Azizzadenesheli, a staff researcher at Nvidia, who provides insights into how LLMs are enhancing RL performance. The article highlights applications like ALOHA, a robot learning to fold clothes, and Voyager, an RL agent using GPT-4 for Minecraft. It also touches upon risk assessment in RL-based decision-making across various domains and the future of deep reinforcement learning, emphasizing the importance of increased computational power for achieving general intelligence.
Reference

“Kamyar shares his insights on how LLMs are pushing RL performance forward in a variety of applications.”

PermalinkPractical AI