Multi-sensor systems are widely used in the Internet of Things, environmental monitoring, and intelligent manufacturing. However, traditional fixed-frequency sampling strategies often lead to severe data redundancy, high energy consumption, and limited adaptability, failing to meet the dynamic sensing needs of complex environments. To address these issues, this paper proposes a DQN-based multi-sensor adaptive sampling optimization method. By leveraging a reinforcement learning framework to learn the optimal sampling strategy, the method balances data quality, energy consumption, and redundancy. We first model the multi-sensor sampling task as a Markov Decision Process (MDP), then employ a Deep Q-Network to optimize the sampling policy. Experiments on the Intel Lab Data dataset confirm that, compared with fixed-frequency sampling, threshold-triggered sampling, and other reinforcement learning approaches, DQN significantly improves data quality while lowering average energy consumption and redundancy rates. Moreover, in heterogeneous multi-sensor environments, DQN-based adaptive sampling shows enhanced robustness, maintaining superior data collection performance even in the presence of interference factors. These findings demonstrate that DQN-based adaptive sampling can enhance overall data acquisition efficiency in multi-sensor systems, providing a new solution for efficient and intelligent sensing.

We investigate encrypted control policy synthesis over the cloud. While encrypted control implementations have been studied previously, we focus on the less explored paradigm of privacy-preserving control synthesis, which can involve heavier computations ideal for cloud outsourcing. We classify control policy synthesis into model-based, simulator-driven, and data-driven approaches and examine their implementation over fully homomorphic encryption (FHE) for privacy enhancements. A key challenge arises from comparison operations (min or max) in standard reinforcement learning algorithms, which are difficult to execute over encrypted data. This observation motivates our focus on Relative-Entropy-regularized reinforcement learning (RL) problems, which simplifies encrypted evaluation of synthesis algorithms due to their comparison-free structures. We demonstrate how linearly solvable value iteration, path integral control, and Z-learning can be readily implemented over FHE. We conduct a case study of our approach through numerical simulations of encrypted Z-learning in a grid world environment using the CKKS encryption scheme, showing convergence with acceptable approximation error. Our work suggests the potential for secure and efficient cloud-based reinforcement learning.

In this letter, we explore the communication-control co-design of discrete-time stochastic linear systems through reinforcement learning. Specifically, we examine a closed-loop system involving two sequential decision-makers: a scheduler and a controller. The scheduler continuously monitors the system's state but transmits it to the controller intermittently to balance the communication cost and control performance. The controller, in turn, determines the control input based on the intermittently received information. Given the partially nested information structure, we show that the optimal control policy follows a certainty-equivalence form. Subsequently, we analyze the qualitative behavior of the scheduling policy. To develop the optimal scheduling policy, we propose InterQ, a deep reinforcement learning algorithm which uses a deep neural network to approximate the Q-function. Through extensive numerical evaluations, we analyze the scheduling landscape and further compare our approach against two baseline strategies: (a) a multi-period periodic scheduling policy, and (b) an event-triggered policy. The results demonstrate that our proposed method outperforms both baselines. The open source implementation can be found at https://github.com/AC-sh/InterQ.

Deep reinforcement learning has achieved superhuman racing performance in high-fidelity simulators like Gran Turismo 7 (GT7). It typically utilizes global features that require instrumentation external to a car, such as precise localization of agents and opponents, limiting real-world applicability. To address this limitation, we introduce a vision-based autonomous racing agent that relies solely on ego-centric camera views and onboard sensor data, eliminating the need for precise localization during inference. This agent employs an asymmetric actor-critic framework: the actor uses a recurrent neural network with the sensor data local to the car to retain track layouts and opponent positions, while the critic accesses the global features during training. Evaluated in GT7, our agent consistently outperforms GT7's built-drivers. To our knowledge, this work presents the first vision-based autonomous racing agent to demonstrate champion-level performance in competitive racing scenarios.

The Treacherous Turn refers to the scenario where an artificial intelligence (AI) agent subtly, and perhaps covertly, learns to perform a behavior that benefits itself but is deemed undesirable and potentially harmful to a human supervisor. During training, the agent learns to behave as expected by the human supervisor, but when deployed to perform its task, it performs an alternate behavior without the supervisor there to prevent it. Initial experiments applying DRL to an implementation of the A Link to the Past example do not produce the treacherous turn effect naturally, despite various modifications to the environment intended to produce it. However, in this work, we find the treacherous behavior to be reproducible in a DRL agent when using other trojan injection strategies. This approach deviates from the prototypical treacherous turn behavior since the behavior is explicitly trained into the agent, rather than occurring as an emergent consequence of environmental complexity or poor objective specification. Nonetheless, these experiments provide new insights into the challenges of producing agents capable of true treacherous turn behavior.

Reward Generation via Large Vision-Language Model in Offline Reinforcement Learning Y ounghwan Lee Electrical Engineering KAIST Daejeon, South Korea youngh2@kaist.ac.kr Chang D. Y oo Electrical Engineering KAIST Daejeon, South Korea cd yoo@kaist.ac.kr Abstract --In offline reinforcement learning (RL), learning from fixed datasets presents a promising solution for domains where real-time interaction with the environment is expensive or risky. However, designing dense reward signals for offline dataset requires significant human effort and domain expertise. Reinforcement learning with human feedback (RLHF) has emerged as an alternative, but it remains costly due to the human-in-the-loop process, prompting interest in automated reward generation models. T o address this, we propose Reward Generation via Large Vision-Language Models (RG-VLM), which leverages the reasoning capabilities of L VLMs to generate rewards from offline data without human involvement.

Recently DeepSeek R1 has shown that reinforcement learning (RL) can substantially improve the reasoning capabilities of Large Language Models (LLMs) through a simple yet effective design. The core of R1 lies in its rule-based reward formulation, which leverages tasks with deterministic ground-truth answers to enable precise and stable reward computation. In the visual domain, we similarly observe that a wide range of visual understanding tasks are inherently equipped with well-defined ground-truth annotations. This property makes them naturally compatible with rule-based reward mechanisms. Motivated by this observation, we investigate the extension of R1-style reinforcement learning to Vision-Language Models (VLMs), aiming to enhance their visual reasoning capabilities. To this end, we develop VLM-R1, a dedicated framework designed to harness RL for improving VLMs' performance on general vision-language tasks. Using this framework, we further explore the feasibility of applying RL to visual domain. Experimental results indicate that the RL-based model not only delivers competitive performance on visual understanding tasks but also surpasses Supervised Fine-Tuning (SFT) in generalization ability. Furthermore, we conduct comprehensive ablation studies that uncover a series of noteworthy insights, including the presence of reward hacking in object detection, the emergence of the "OD aha moment", the impact of training data quality, and the scaling behavior of RL across different model sizes. Through these analyses, we aim to deepen the understanding of how reinforcement learning enhances the capabilities of vision-language models, and we hope our findings and open-source contributions will support continued progress in the vision-language RL community. Our code and model are available at https://github.com/om-ai-lab/VLM-R1

This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible. Multi-agent task allocation (MATA) plays a vital role in cooperative multi-agent systems, with significant implications for applications such as logistics, search and rescue, and robotic coordination. Although traditional deep reinforcement learning (DRL) methods have been shown to be promising, their effectiveness is hindered by a reliance on manually designed reward functions and inefficiencies in dynamic environments. In this paper, an inverse reinforcement learning (IRL)-based framework is proposed, in which multi-head self-attention (MHSA) and graph attention mechanisms are incorporated to enhance reward function learning and task execution efficiency. Expert demonstrations are utilized to infer optimal reward densities, allowing dependence on handcrafted designs to be reduced and adaptability to be improved. Extensive experiments validate the superiority of the proposed method over widely used multi-agent reinforcement learning (MARL) algorithms in terms of both cumulative rewards and task execution efficiency.

Recent advances in deep neural language models combined wit h the capacity of large scale datasets have accelerated the development of natural langu age generation systems that produce fluent and coherent texts (to various degrees of succ ess) in a multitude of tasks and application contexts. However, controlling the output of t hese models for specific user and task needs is still an open challenge. This is crucial not onl y to customizing the content and style of the generated language, but also to their safe and re liable deployment in the real world. We present an extensive survey on the emerging topic o f constrained neural language generation in which we formally define and categorize the pro blems of natural language generation by distinguishing between conditions and constraints (the latter being testable conditions on the output text instead of the input), present constrained text generation tasks, and review existing methods and evaluation metrics for cons trained text generation. Our aim is to highlight recent progress and trends in this emergi ng field, informing on the most promising directions and limitations towards advancing th e state-of-the-art of constrained neural language generation research.

This paper introduces a sensor steering methodology based on deep reinforcement learning to enhance the predictive accuracy and decision support capabilities of digital twins by optimising the data acquisition process. Traditional sensor placement techniques are often constrained by one-off optimisation strategies, which limit their applicability for online applications requiring continuous informative data assimilation. The proposed approach addresses this limitation by offering an adaptive framework for sensor placement within the digital twin paradigm. The sensor placement problem is formulated as a Markov decision process, enabling the training and deployment of an agent capable of dynamically repositioning sensors in response to the evolving conditions of the physical structure as represented by the digital twin. This ensures that the digital twin maintains a highly representative and reliable connection to its physical counterpart. The proposed framework is validated through a series of comprehensive case studies involving a cantilever plate structure subjected to diverse conditions, including healthy and damaged conditions. The results demonstrate the capability of the deep reinforcement learning agent to adaptively reposition sensors improving the quality of data acquisition and hence enhancing the overall accuracy of digital twins.