arXiv subjects

Chao Yu

Publications and source records attributed to Chao Yu.

At least 109 records · Page 6Linked to original sources

arXiv2025

Differential absorption ozone Lidar with 4H-SiC single-photon detectors

Differential absorption Lidar (DIAL) in the ultraviolet (UV) region is an effective approach for monitoring tropospheric ozone. 4H-SiC single-photon detectors (SPDs) are emergent devices for UV single-photon detection. Here, we demonstrate a 4H-SiC SPD-based ozone DIAL. We design and fabricate the 4H-SiC single-photon avalanche diode with a beveled mesa structure and optimized layer thickness. An active quenching circuit with a quenching time of 1.03 ns is developed to significantly mitigate the afterpulsing effect while enhancing the maximum count rate. After characterization, the SPD exhibits excellent performance with a photon detection efficiency of 16.6% at 266 nm, a dark count rate of 138 kcps, a maximum count rate of 13 Mcps, and an afterpulse probability of 2.7% at room temperature. Then, we apply two 4H-SiC SPDs in an ozone DIAL. The measured ozone concentrations at altitudes of 1-3.5 km agree well with the results of a commercial ozone DIAL. Our work provides an alternative solution for general UV Lidar applications.

physics.optics↗

arXiv2025

Compact and fully functional high-frequency sine wave gating InGaAs/InP single-photon detector module

High-frequency sine wave gating (SWG) InGaAs/InP single-photon detectors (SPDs) are widely used for synchronous near-infrared single-photon detection. For practical use, the size of SPD is one of the most concerning features for system integration. Here we present, to the best of our knowledge, the most compact and fully functional high-frequency SWG InGaAs/InP SPD. We develop a sine wave gating integrated circuit (SWGIC) using system-in-package technology that supports functions including large amplitude sine wave gate generation, coincidence gate generation, phase regulation, amplitude monitoring, and amplitude modulation. Moreover, we design and fabricate a high-performance multi-mode fiber coupled InGaAs/InP single-photon avalanche diode (SPAD) with a compact butterfly package. Furthermore, we implement a monolithically integrated readout circuit (MIRC) to extract the weak avalanche signal from large capacitance response of SWG. Finally, the SWGIC, SPAD, MIRC, and the affiliated circuits are integrated into a single module with a size of 6 cm x 5.7 cm x 1.7 cm. After characterization, the SPD module exhibits a photon detection efficiency of 40%, a dark count rate of 9 kcps, and an afterpulse probability of 4.6% at an operation temperature of 238 K and a hold-off time of 160 ns. Our work provides a practical solution for applications necessitating highly integrated near-infrared single-photon detection.

physics.optics↗

arXiv2025

Multi-UAV Formation Control with Static and Dynamic Obstacle Avoidance via Reinforcement Learning

This paper tackles the challenging task of maintaining formation among multiple unmanned aerial vehicles (UAVs) while avoiding both static and dynamic obstacles during directed flight. The complexity of the task arises from its multi-objective nature, the large exploration space, and the sim-to-real gap. To address these challenges, we propose a two-stage reinforcement learning (RL) pipeline. In the first stage, we randomly search for a reward function that balances key objectives: directed flight, obstacle avoidance, formation maintenance, and zero-shot policy deployment. The second stage applies this reward function to more complex scenarios and utilizes curriculum learning to accelerate policy training. Additionally, we incorporate an attention-based observation encoder to improve formation maintenance and adaptability to varying obstacle densities. Experimental results in both simulation and real-world environments demonstrate that our method outperforms both planning-based and RL-based baselines in terms of collision-free rates and formation maintenance across static, dynamic, and mixed obstacle scenarios. Ablation studies further confirm the effectiveness of our curriculum learning strategy and attention-based encoder. Animated demonstrations are available at: https://sites.google.com/view/ uav-formation-with-avoidance/.

cs.RO↗

arXiv2025

Reinforcement Learning with Knowledge Representation and Reasoning: A Brief Survey

Reinforcement Learning (RL) has achieved tremendous development in recent years, but still faces significant obstacles in addressing complex real-life problems due to the issues of poor system generalization, low sample efficiency as well as safety and interpretability concerns. The core reason underlying such dilemmas can be attributed to the fact that most of the work has focused on the computational aspect of value functions or policies using a representational model to describe atomic components of rewards, states and actions etc, thus neglecting the rich high-level declarative domain knowledge of facts, relations and rules that can be either provided a priori or acquired through reasoning over time. Recently, there has been a rapidly growing interest in the use of Knowledge Representation and Reasoning (KRR) methods, usually using logical languages, to enable more abstract representation and efficient learning in RL. In this survey, we provide a preliminary overview on these endeavors that leverage the strengths of KRR to help solving various problems in RL, and discuss the challenging open problems and possible directions for future work in this area.

cs.AI↗

arXiv2024

Rapid Learning in Constrained Minimax Games with Negative Momentum

In this paper, we delve into the utilization of the negative momentum technique in constrained minimax games. From an intuitive mechanical standpoint, we introduce a novel framework for momentum buffer updating, which extends the findings of negative momentum from the unconstrained setting to the constrained setting and provides a universal enhancement to the classic game-solver algorithms. Additionally, we provide theoretical guarantee of convergence for our momentum-augmented algorithms with entropy regularizer. We then extend these algorithms to their extensive-form counterparts. Experimental results on both Normal Form Games (NFGs) and Extensive Form Games (EFGs) demonstrate that our momentum techniques can significantly improve algorithm performance, surpassing both their original versions and the SOTA baselines by a large margin.

cs.LG↗

arXiv2024

An Experimental Study of Passive UAV Tracking with Digital Arrays and Cellular Downlink Signals

Given the prospects of the low-altitude economy (LAE) and the popularity of unmanned aerial vehicles (UAVs), there are increasing demands on monitoring flying objects at low altitude in wide urban areas. In this work, the widely deployed long-term evolution (LTE) base station (BS) is exploited to illuminate UAVs in bistatic trajectory tracking. Specifically, a passive sensing receiver with two digital antenna arrays is proposed and developed to capture both the line-of-sight (LoS) signal and the scattered signal off a target UAV. From their cross ambiguity function, the bistatic range, Doppler shift and angle-of-arrival (AoA) of the target UAV can be detected in a sequence of time slots. In order to address missed detections and false alarms of passive sensing, a multi-target tracking framework is adopted to track the trajectory of the target UAV. It is demonstrated by experiments that the proposed UAV tracking system can achieve a meter-level accuracy.

eess.SP↗

arXiv2024

Hierarchical Multi-agent Meta-Reinforcement Learning for Cross-channel Bidding

Real-time bidding (RTB) plays a pivotal role in online advertising ecosystems. Advertisers employ strategic bidding to optimize their advertising impact while adhering to various financial constraints, such as the return-on-investment (ROI) and cost-per-click (CPC). Primarily focusing on bidding with fixed budget constraints, traditional approaches cannot effectively manage the dynamic budget allocation problem where the goal is to achieve global optimization of bidding performance across multiple channels with a shared budget. In this paper, we propose a hierarchical multi-agent reinforcement learning framework for multi-channel bidding optimization. In this framework, the top-level strategy applies a CPC constrained diffusion model to dynamically allocate budgets among the channels according to their distinct features and complex interdependencies, while the bottom-level strategy adopts a state-action decoupled actor-critic method to address the problem of extrapolation errors in offline learning caused by out-of-distribution actions and a context-based meta-channel knowledge learning method to improve the state representation capability of the policy based on the shared knowledge among different channels. Comprehensive experiments conducted on a large scale real-world industrial dataset from the Meituan ad bidding platform demonstrate that our method achieves a state-of-the-art performance.

cs.AI↗

arXiv2024

Offline Multi-Agent Reinforcement Learning via In-Sample Sequential Policy Optimization

Offline Multi-Agent Reinforcement Learning (MARL) is an emerging field that aims to learn optimal multi-agent policies from pre-collected datasets. Compared to single-agent case, multi-agent setting involves a large joint state-action space and coupled behaviors of multiple agents, which bring extra complexity to offline policy optimization. In this work, we revisit the existing offline MARL methods and show that in certain scenarios they can be problematic, leading to uncoordinated behaviors and out-of-distribution (OOD) joint actions. To address these issues, we propose a new offline MARL algorithm, named In-Sample Sequential Policy Optimization (InSPO). InSPO sequentially updates each agent's policy in an in-sample manner, which not only avoids selecting OOD joint actions but also carefully considers teammates' updated policies to enhance coordination. Additionally, by thoroughly exploring low-probability actions in the behavior policy, InSPO can well address the issue of premature convergence to sub-optimal solutions. Theoretically, we prove InSPO guarantees monotonic policy improvement and converges to quantal response equilibrium (QRE). Experimental results demonstrate the effectiveness of our method compared to current state-of-the-art offline MARL methods.

cs.AI↗

arXiv2024

MASP: Scalable GNN-based Planning for Multi-Agent Navigation

We investigate multi-agent navigation tasks, where multiple agents need to reach initially unassigned goals in a limited time. Classical planning-based methods suffer from expensive computation overhead at each step and offer limited expressiveness for complex cooperation strategies. In contrast, reinforcement learning (RL) has recently become a popular approach for addressing this issue. However, RL struggles with low data efficiency and cooperation when directly exploring (nearly) optimal policies in a large exploration space, especially with an increased number of agents(e.g., 10+ agents) or in complex environments (e.g., 3-D simulators). In this paper, we propose the Multi-Agent Scalable Graph-based Planner (MASP), a goal-conditioned hierarchical planner for navigation tasks with a substantial number of agents in the decentralized setting. MASP employs a hierarchical framework to reduce space complexity by decomposing a large exploration space into multiple goal-conditioned subspaces, where a high-level policy assigns agents goals, and a low-level policy navigates agents toward designated goals. For agent cooperation and the adaptation to varying team sizes, we model agents and goals as graphs to better capture their relationship. The high-level policy, the Goal Matcher, leverages a graph-based Self-Encoder and Cross-Encoder to optimize goal assignment by updating the agent and the goal graphs. The low-level policy, the Coordinated Action Executor, introduces the Group Information Fusion to facilitate group division and extract agent relationships across groups, enhancing training efficiency for agent cooperation. The results demonstrate that MASP outperforms RL and planning-based baselines in task efficiency.

cs.LG↗

arXiv2024

Revisiting Discrete Soft Actor-Critic

We study the adaption of Soft Actor-Critic (SAC), which is considered as a state-of-the-art reinforcement learning (RL) algorithm, from continuous action space to discrete action space. We revisit vanilla discrete SAC and provide an in-depth understanding of its Q value underestimation and performance instability issues when applied to discrete settings. We thereby propose Stable Discrete SAC (SDSAC), an algorithm that leverages entropy-penalty and double average Q-learning with Q-clip to address these issues. Extensive experiments on typical benchmarks with discrete action space, including Atari games and a large-scale MOBA game, show the efficacy of our proposed method. Our code is at: https://github.com/coldsummerday/SD-SAC.git.

cs.LG↗

arXiv2024

Hunyuan-Large: An Open-Source MoE Model with 52 Billion Activated Parameters by Tencent

In this paper, we introduce Hunyuan-Large, which is currently the largest open-source Transformer-based mixture of experts model, with a total of 389 billion parameters and 52 billion activation parameters, capable of handling up to 256K tokens. We conduct a thorough evaluation of Hunyuan-Large's superior performance across various benchmarks including language understanding and generation, logical reasoning, mathematical problem-solving, coding, long-context, and aggregated tasks, where it outperforms LLama3.1-70B and exhibits comparable performance when compared to the significantly larger LLama3.1-405B model. Key practice of Hunyuan-Large include large-scale synthetic data that is orders larger than in previous literature, a mixed expert routing strategy, a key-value cache compression technique, and an expert-specific learning rate strategy. Additionally, we also investigate the scaling laws and learning rate schedule of mixture of experts models, providing valuable insights and guidances for future model development and optimization. The code and checkpoints of Hunyuan-Large are released to facilitate future innovations and applications. Codes: https://github.com/Tencent/Hunyuan-Large Models: https://huggingface.co/tencent/Tencent-Hunyuan-Large

cs.CL↗

arXiv2024

SleepNetZero: Zero-Burden Zero-Shot Reliable Sleep Staging With Neural Networks Based on Ballistocardiograms

Sleep monitoring plays a crucial role in maintaining good health, with sleep staging serving as an essential metric in the monitoring process. Traditional methods, utilizing medical sensors like EEG and ECG, can be effective but often present challenges such as unnatural user experience, complex deployment, and high costs. Ballistocardiography~(BCG), a type of piezoelectric sensor signal, offers a non-invasive, user-friendly, and easily deployable alternative for long-term home monitoring. However, reliable BCG-based sleep staging is challenging due to the limited sleep monitoring data available for BCG. A restricted training dataset prevents the model from generalization across populations. Additionally, transferring to BCG faces difficulty ensuring model robustness when migrating from other data sources. To address these issues, we introduce SleepNetZero, a zero-shot learning based approach for sleep staging. To tackle the generalization challenge, we propose a series of BCG feature extraction methods that align BCG components with corresponding respiratory, cardiac, and movement channels in PSG. This allows models to be trained on large-scale PSG datasets that are diverse in population. For the migration challenge, we employ data augmentation techniques, significantly enhancing generalizability. We conducted extensive training and testing on large datasets~(12393 records from 9637 different subjects), achieving an accuracy of 0.803 and a Cohen's Kappa of 0.718. ZeroSleepNet was also deployed in real prototype~(monitoring pads) and tested in actual hospital settings~(265 users), demonstrating an accuracy of 0.697 and a Cohen's Kappa of 0.589. To the best of our knowledge, this work represents the first known reliable BCG-based sleep staging effort and marks a significant step towards in-home health monitoring.

eess.SP↗

arXiv2024

Diverse Policies Recovering via Pointwise Mutual Information Weighted Imitation Learning

Recovering a spectrum of diverse policies from a set of expert trajectories is an important research topic in imitation learning. After determining a latent style for a trajectory, previous diverse policies recovering methods usually employ a vanilla behavioral cloning learning objective conditioned on the latent style, treating each state-action pair in the trajectory with equal importance. Based on an observation that in many scenarios, behavioral styles are often highly relevant with only a subset of state-action pairs, this paper presents a new principled method in diverse polices recovery. In particular, after inferring or assigning a latent style for a trajectory, we enhance the vanilla behavioral cloning by incorporating a weighting mechanism based on pointwise mutual information. This additional weighting reflects the significance of each state-action pair's contribution to learning the style, thus allowing our method to focus on state-action pairs most representative of that style. We provide theoretical justifications for our new objective, and extensive empirical evaluations confirm the effectiveness of our method in recovering diverse policies from expert data.

cs.LG↗

arXiv2024

Reward-Robust RLHF in LLMs

As Large Language Models (LLMs) continue to progress toward more advanced forms of intelligence, Reinforcement Learning from Human Feedback (RLHF) is increasingly seen as a key pathway toward achieving Artificial General Intelligence (AGI). However, the reliance on reward-model-based (RM-based) alignment methods introduces significant challenges due to the inherent instability and imperfections of Reward Models (RMs), which can lead to critical issues such as reward hacking and misalignment with human intentions. In this paper, we introduce a reward-robust RLHF framework aimed at addressing these fundamental challenges, paving the way for more reliable and resilient learning in LLMs. Our approach introduces a novel optimization objective that carefully balances performance and robustness by incorporating Bayesian Reward Model Ensembles (BRME) to model the uncertainty set of reward functions. This allows the framework to integrate both nominal performance and minimum reward signals, ensuring more stable learning even with imperfect RMs. Empirical results demonstrate that our framework consistently outperforms baselines across diverse benchmarks, showing improved accuracy and long-term stability. We also provide a theoretical analysis, demonstrating that reward-robust RLHF approaches the stability of constant reward settings, which proves to be acceptable even in a stochastic-case analysis. Together, these contributions highlight the framework potential to enhance both the performance and stability of LLM alignment.

cs.LG↗

arXiv2024

Is DPO Superior to PPO for LLM Alignment? A Comprehensive Study

Reinforcement Learning from Human Feedback (RLHF) is currently the most widely used method to align large language models (LLMs) with human preferences. Existing RLHF methods can be roughly categorized as either reward-based or reward-free. Novel applications such as ChatGPT and Claude leverage reward-based methods that first learn a reward model and apply actor-critic algorithms, such as Proximal Policy Optimization (PPO). However, in academic benchmarks, state-of-the-art results are often achieved via reward-free methods, such as Direct Preference Optimization (DPO). Is DPO truly superior to PPO? Why does PPO perform poorly on these benchmarks? In this paper, we first conduct both theoretical and empirical studies on the algorithmic properties of DPO and show that DPO may have fundamental limitations. Moreover, we also comprehensively examine PPO and reveal the key factors for the best performances of PPO in fine-tuning LLMs. Finally, we benchmark DPO and PPO across a collection of RLHF testbeds, ranging from dialogue to code generation. Experiment results demonstrate that PPO is able to surpass other alignment methods in all cases and achieve state-of-the-art results in challenging code competitions. Our code is publicly available at https://github.com/openpsi-project/ReaLHF.

cs.CL↗

arXiv2024

Trajectory Tracking for MmWave Communication Systems via Cooperative Passive Sensing

In this paper, a cooperative passive sensing framework for millimeter wave (mmWave) communication systems is proposed and demonstrated in a scenario with one mobile signal blocker. Specifically, in the uplink communication with at least two transmitters, a cooperative detection method is proposed for the receiver to track the blocker's trajectory, localize the transmitters and detect the potential link blockage jointly. To facilitate detection, the receiver collects the signal of each transmitter along a line-of-sight (LoS) path and a non-line-of-sight (NLoS) path separately via two narrow-beam phased arrays. The latter path should scatter at the mobile blocker, and hence it can be identified by the Doppler frequency. Comparing the received signals of both paths, the Doppler frequency and angle-of-arrival (AoA) of the NLoS path can be estimated. To resolve the blocker's trajectory and the transmitters' locations, the receiver should continuously track the mobile blocker to accumulate sufficient numbers of the Doppler frequency and AoA versus time observations. Finally, a gradient-descent-based algorithm is proposed for joint detection. With the reconstructed trajectory, the potential link blockage can be predicted. It is demonstrated that the system can achieve decimeter-level localization and trajectory estimation, and predict the blockage time with an error less than 0.1 s.

eess.SP↗

arXiv2024

An Offline Adaptation Framework for Constrained Multi-Objective Reinforcement Learning

In recent years, significant progress has been made in multi-objective reinforcement learning (RL) research, which aims to balance multiple objectives by incorporating preferences for each objective. In most existing studies, specific preferences must be provided during deployment to indicate the desired policies explicitly. However, designing these preferences depends heavily on human prior knowledge, which is typically obtained through extensive observation of high-performing demonstrations with expected behaviors. In this work, we propose a simple yet effective offline adaptation framework for multi-objective RL problems without assuming handcrafted target preferences, but only given several demonstrations to implicitly indicate the preferences of expected policies. Additionally, we demonstrate that our framework can naturally be extended to meet constraints on safety-critical objectives by utilizing safe demonstrations, even when the safety thresholds are unknown. Empirical results on offline multi-objective and safe tasks demonstrate the capability of our framework to infer policies that align with real preferences while meeting the constraints implied by the provided demonstrations.

cs.LG↗

arXiv2024

Recent advances in InGaAs/InP single-photon detectors

Single-photon detectors (SPDs) are widely used in applications requiring extremely weak light detection. In the near-infrared region, SPDs based on InGaAs/InP single-photon avalanche diodes (SPADs) are the primary candidates for practical applications because of their small size, low cost and ease of operation. Driven by the escalating demands for quantum communication and lidar, the performance of InGaAs/InP SPDs has been continuously enhanced. This paper provides a comprehensive review of advances in InGaAs/InP SPDs over the past 10 years, including the investigation into SPAD structures and mechanisms, as well as emerging readout techniques for both gated and free-running mode SPDs. In addition, future prospects are also summarised.

physics.ins-det↗