A Parallel Hybrid Action Space Reinforcement Learning Model for Real-world Adaptive Traffic Signal Control

• URL: http://arxiv.org/abs/2503.14250v1
• Date: Tue, 18 Mar 2025 13:38:53 GMT
• Title: A Parallel Hybrid Action Space Reinforcement Learning Model for Real-world Adaptive Traffic Signal Control
• Abstract summary: We introduce a parallel hybrid action space reinforcement learning model (PH-DDPG) that optimize traffic signal phase and duration of traffic signals simultaneously.<n>Our model features a task-specific parallel hybrid action space tailored for adaptive traffic control, which directly outputs discrete phase selections and their associated continuous duration parameters concurrently.
• Score: 10.9431172488753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Adaptive traffic signal control (ATSC) can effectively reduce vehicle travel times by dynamically adjusting signal timings but poses a critical challenge in real-world scenarios due to the complexity of real-time decision-making in dynamic and uncertain traffic conditions. The burgeoning field of intelligent transportation systems, bolstered by artificial intelligence techniques and extensive data availability, offers new prospects for the implementation of ATSC. In this study, we introduce a parallel hybrid action space reinforcement learning model (PH-DDPG) that optimizes traffic signal phase and duration of traffic signals simultaneously, eliminating the need for sequential decision-making seen in traditional two-stage models. Our model features a task-specific parallel hybrid action space tailored for adaptive traffic control, which directly outputs discrete phase selections and their associated continuous duration parameters concurrently, thereby inherently addressing dynamic traffic adaptation through unified parametric optimization. %Our model features a unique parallel hybrid action space that allows for the simultaneous output of each action and its optimal parameters, streamlining the decision-making process. Furthermore, to ascertain the robustness and effectiveness of this approach, we executed ablation studies focusing on the utilization of a random action parameter mask within the critic network, which decouples the parameter space for individual actions, facilitating the use of preferable parameters for each action. The results from these studies confirm the efficacy of this method, distinctly enhancing real-world applicability

Related papers

• Hierarchically Decoupled Mixture-of-Experts for Robust Traffic Sign Recognition in Complex Driving Scenarios [6.121863091287242]
  CBDES MoE TSR is a hierarchically decoupled heterogeneous mixture-of-experts(MoE) framework for traffic sign recognition.<n>Based on the semantic characteristics of the input image, the gating module selectively activates the most suitable expert model from the expert pool.<n> Experimental results demonstrate that the proposed method achieves a remarkable balance between detection accuracy and efficiency.
  arXiv  Detail & Related papers  (2026-06-01T07:39:44Z)
• From Noise to Control: Parameterized Diffusion Policies [54.941343992110426]
  We propose a framework for learning diffusion policies conditioned on continuous parameters embedded in a learned behavior manifold.<n>Our approach enables smooth distances between known strategies and efficient adaptation to novel constraints without updating policy weights.
  arXiv  Detail & Related papers  (2026-05-29T20:21:50Z)
• SEATrack: Simple, Efficient, and Adaptive Multimodal Tracker [48.348918789044]
  We introduce SEATrack, a Simple, Efficient, and Adaptive two-stream multimodal tracker.<n> AMG-LoRA seamlessly integrates Low-Rank Adaptation (LoRA) for domain adaptation with Adaptive Mutual Guidance ( AMG) to dynamically refine and align attention maps.<n>We then depart from conventional local fusion approaches by introducing a Hierarchical Mixture of Experts (HMoE) that enables efficient global relation modeling.
  arXiv  Detail & Related papers  (2026-04-14T09:27:50Z)
• Spatiotemporal Decision Transformer for Traffic Coordination [1.2099551931618155]
  MADT (Multi-Agent Decision Transformer) is a novel approach that reformulates multi-agent traffic signal control as a sequence modeling problem.<n>Our approach enables offline learning from historical traffic data, with architecture design that facilitates potential online fine-tuning.
  arXiv  Detail & Related papers  (2026-02-02T23:19:13Z)
• Adaptive Linear Path Model-Based Diffusion [52.84663832658799]
  We introduce Linear Path Model-Based Diffusion (LP-MBD), which replaces the variance-preserving schedule with a flow-matching linear probability path.<n>We also propose Adaptive LP-MBD (ALP-MBD), which leverages reinforcement learning to adjust diffusion steps and noise levels according to task complexity and environmental conditions.
  arXiv  Detail & Related papers  (2026-02-02T21:33:03Z)
• Model-Based Diffusion Sampling for Predictive Control in Offline Decision Making [48.998030470623384]
  offline decision-making requires reliable behaviors from fixed datasets without further interaction.<n>We propose a compositional model-based diffusion framework consisting of: (i) a planner that generates diverse, task-aligned trajectories; (ii) a dynamics model that enforces consistency with the underlying system dynamics; and (iii) a ranker module that selects behaviors aligned with the task objectives.
  arXiv  Detail & Related papers  (2025-12-09T06:26:02Z)
• Optimization-Guided Diffusion for Interactive Scene Generation [52.23368750264419]
  We present OMEGA, an optimization-guided, training-free framework that enforces structural consistency and interaction awareness during diffusion-based sampling.<n>We show that OMEGA improves generation realism, consistency, and controllability, increasing the ratio of physically and behaviorally valid scenes.<n>Our approach can also generate $5times$ more near-collision frames with a time-to-collision under three seconds.
  arXiv  Detail & Related papers  (2025-12-08T15:56:18Z)
• Robust Single-Agent Reinforcement Learning for Regional Traffic Signal Control Under Demand Fluctuations [5.784337914162491]
  Traffic congestion, primarily driven by intersection queuing, significantly impacts urban living standards, safety, environmental quality, and economic efficiency.<n>This study introduces a novel single-agent reinforcement learning framework for regional adaptive TSC.<n>The framework exhibits robust anti-fluctuation capability and significantly reduces queue lengths.
  arXiv  Detail & Related papers  (2025-11-01T13:18:50Z)
• Fints: Efficient Inference-Time Personalization for LLMs with Fine-Grained Instance-Tailored Steering [49.212940215720884]
  We propose a steering framework that generates sample-level interference from user data and injects it into the model's forward pass for personalized adaptation.<n>Our method significantly enhances personalization performance in fast-shifting environments while maintaining robustness across varying interaction modes and context lengths.
  arXiv  Detail & Related papers  (2025-10-31T06:01:04Z)
• BIDA: A Bi-level Interaction Decision-making Algorithm for Autonomous Vehicles in Dynamic Traffic Scenarios [5.193590097161461]
  We design a bi-level interaction decision-making algorithm (BIDA) that integrates interactive Monte Carlo tree search (MCTS) with deep reinforcement learning (DRL)<n>Specifically, we adopt three types of DRL algorithms to construct a reliable value network and policy network, which guide the online deduction process of interactive MCTS.<n> Experimental evaluations demonstrate that our BIDA not only enhances interactive deduction and reduces computational costs, but also outperforms other latest benchmarks.
  arXiv  Detail & Related papers  (2025-06-19T19:03:40Z)
• Latent Adaptive Planner for Dynamic Manipulation [44.885020943751464]
  Latent Adaptive Planner (LAP) is a novel approach for dynamic nonprehensile manipulation tasks.<n>LAP formulates planning as latent space inference, effectively learned from human demonstration videos.
  arXiv  Detail & Related papers  (2025-05-06T00:09:09Z)
• World Model-Based Learning for Long-Term Age of Information Minimization in Vehicular Networks [53.98633183204453]
  In this paper, a novel world model-based learning framework is proposed to minimize packet-completeness-aware age of information (CAoI) in a vehicular network.<n>A world model framework is proposed to jointly learn a dynamic model of the mmWave V2X environment and use it to imagine trajectories for learning how to perform link scheduling.<n>In particular, the long-term policy is learned in differentiable imagined trajectories instead of environment interactions.
  arXiv  Detail & Related papers  (2025-05-03T06:23:18Z)
• CoLLMLight: Cooperative Large Language Model Agents for Network-Wide Traffic Signal Control [7.0964925117958515]
  Traffic Signal Control (TSC) plays a critical role in urban traffic management by optimizing traffic flow and mitigating congestion.<n>Existing approaches fail to address the essential need for inter-agent coordination.<n>We propose CoLLMLight, a cooperative LLM agent framework for TSC.
  arXiv  Detail & Related papers  (2025-03-14T15:40:39Z)
• End-to-End Predictive Planner for Autonomous Driving with Consistency Models [5.966385886363771]
  Trajectory prediction and planning are fundamental components for autonomous vehicles to navigate safely and efficiently in dynamic environments. Traditionally, these components have often been treated as separate modules, limiting the ability to perform interactive planning. We present a novel unified and data-driven framework that integrates prediction and planning with a single consistency model.
  arXiv  Detail & Related papers  (2025-02-12T00:26:01Z)
• Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models [57.45019514036948]
  Simultaneous MRMP Diffusion (SMD) is a novel approach integrating constrained optimization into the diffusion sampling process to produce kinematically feasible trajectories. The paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints.
  arXiv  Detail & Related papers  (2025-02-05T20:51:28Z)
• Merging Models on the Fly Without Retraining: A Sequential Approach to Scalable Continual Model Merging [75.93960998357812]
  Deep model merging represents an emerging research direction that combines multiple fine-tuned models to harness their capabilities across different tasks and domains.<n>Current model merging techniques focus on merging all available models simultaneously, with weight matrices-based methods being the predominant approaches.<n>We propose a training-free projection-based continual merging method that processes models sequentially.
  arXiv  Detail & Related papers  (2025-01-16T13:17:24Z)
• Multi-Agent Path Finding in Continuous Spaces with Projected Diffusion Models [57.45019514036948]
  Multi-Agent Path Finding (MAPF) is a fundamental problem in robotics. This work proposes a novel approach that integrates constrained optimization with diffusion models for MAPF in continuous spaces.
  arXiv  Detail & Related papers  (2024-12-23T21:27:19Z)
• End-to-end Driving in High-Interaction Traffic Scenarios with Reinforcement Learning [24.578178308010912]
  We propose an end-to-end model-based RL algorithm named Ramble to address these issues. By learning a dynamics model of the environment, Ramble can foresee upcoming traffic events and make more informed, strategic decisions. Ramble achieves state-of-the-art performance regarding route completion rate and driving score on the CARLA Leaderboard 2.0, showcasing its effectiveness in managing complex and dynamic traffic situations.
  arXiv  Detail & Related papers  (2024-10-03T06:45:59Z)
• MTDT: A Multi-Task Deep Learning Digital Twin [8.600701437207725]
  We introduce the Multi-Task Deep Learning Digital Twin (MTDT) as a solution for multifaceted and precise intersection traffic flow simulation. MTDT enables accurate, fine-grained estimation of loop detector waveform time series for each lane of movement. By consolidating the learning process across multiple tasks, MTDT demonstrates reduced overfitting, increased efficiency, and enhanced effectiveness.
  arXiv  Detail & Related papers  (2024-05-02T00:34:10Z)
• SAFE-SIM: Safety-Critical Closed-Loop Traffic Simulation with Diffusion-Controllable Adversaries [94.84458417662407]
  We introduce SAFE-SIM, a controllable closed-loop safety-critical simulation framework. Our approach yields two distinct advantages: 1) generating realistic long-tail safety-critical scenarios that closely reflect real-world conditions, and 2) providing controllable adversarial behavior for more comprehensive and interactive evaluations. We validate our framework empirically using the nuScenes and nuPlan datasets across multiple planners, demonstrating improvements in both realism and controllability.
  arXiv  Detail & Related papers  (2023-12-31T04:14:43Z)
• RLPG: Reinforcement Learning Approach for Dynamic Intra-Platoon Gap Adaptation for Highway On-Ramp Merging [14.540226579203207]
  A platoon refers to a group of vehicles traveling together in very close proximity. Recent research has revealed a detrimental effect of the extremely small intra-platoon gap on traffic flow for highway on-ramp merging. We present a novel reinforcement learning framework that adaptively adjusts the intra-platoon gap of an individual platoon member to maximize traffic flow.
  arXiv  Detail & Related papers  (2022-12-07T07:33:54Z)
• Data efficient reinforcement learning and adaptive optimal perimeter control of network traffic dynamics [0.0]
  This work proposes an integral reinforcement learning (IRL) based approach to learning the macroscopic traffic dynamics for adaptive optimal perimeter control. To reduce the sampling complexity and use the available data more efficiently, the experience replay (ER) technique is introduced to the IRL algorithm. The convergence of the IRL-based algorithms and the stability of the controlled traffic dynamics are proven via the Lyapunov theory.
  arXiv  Detail & Related papers  (2022-09-13T04:28:49Z)
• Multi-intersection Traffic Optimisation: A Benchmark Dataset and a Strong Baseline [85.9210953301628]
  Control of traffic signals is fundamental and critical to alleviate traffic congestion in urban areas. Because of the high complexity of modelling the problem, experimental settings of current works are often inconsistent. We propose a novel and strong baseline model based on deep reinforcement learning with the encoder-decoder structure.
  arXiv  Detail & Related papers  (2021-01-24T03:55:39Z)
• Spatial-Temporal Transformer Networks for Traffic Flow Forecasting [74.76852538940746]
  We propose a novel paradigm of Spatial-Temporal Transformer Networks (STTNs) to improve the accuracy of long-term traffic forecasting. Specifically, we present a new variant of graph neural networks, named spatial transformer, by dynamically modeling directed spatial dependencies. The proposed model enables fast and scalable training over a long range spatial-temporal dependencies.
  arXiv  Detail & Related papers  (2020-01-09T10:21:04Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.