From Prior-Guided Heuristics to Deployable Agents: Accelerating Demonstration-Driven Reinforcement Learning for Deadline-Constrained Network Control

• URL: http://arxiv.org/abs/2609.03590v1
• Date: Thu, 03 Sep 2026 09:36:56 GMT
• Title: From Prior-Guided Heuristics to Deployable Agents: Accelerating Demonstration-Driven Reinforcement Learning for Deadline-Constrained Network Control
• Abstract summary: This paper introduces a deployment-focused network control framework that addresses both obstacles.<n>First, we present Effective Congestion (EC), a deadline-aware metric family that quantifies interface congestion by packet urgency.<n>Second, we introduce a unified training objective that generalizes existing policy-learning paradigms.
• Score: 17.40703988810448
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Timely delivery of delay-sensitive information over dynamic, heterogeneous networks is essential for NextG interactive applications, yet providing strict End-to-End (E2E) peak latency guarantees remains an open challenge. Two obstacles limit the adoption of learning-based network control in this setting: traditional volume-based routing metrics, while highly effective for general traffic management, are not designed to capture traffic urgency; and Deep Reinforcement Learning (DRL) controllers trained from scratch suffer from sample inefficiency, long training times, and early-stage exploration volatility. This paper introduces a deployment-focused network control framework that addresses both obstacles. First, we present Effective Congestion (EC), a deadline-aware metric family that quantifies interface congestion by packet urgency and proactively filters non-viable traffic, coupled with a Uniform Path Grouping (UPG) distribution heuristic promoting robust load-balancing; the resulting policies are embedded into Multi-Agent Deep Reinforcement Learning Effective Congestion ($p^*$) (MADRL EC ($p^*$)), a hybrid architecture combining a distributed scheduler with a centralized RL-based router. Second, we introduce a unified training objective that generalizes existing policy-learning paradigms---behavioral cloning, offline Reinforcement Learning (RL), online RL, and offline-to-online schemes---as special cases, combining a live-reward term, a pre-collected-reward term, and a policy-imitation term. From this objective, we derive the Model-Guided Annealed Reinforcement Learning (MGA-RL) protocol, instantiated on a Deep Deterministic Policy Gradient (DDPG) backbone: a deployment-oriented, demonstration-driven training approach that generalizes conventional Offline-to-Online (O2O) schemes, in which trajectories from a lightweight [...]

Related papers

• MA-HEAD-Net: Adaptive Rule-Guided Multi-Agent DRL for AoI Minimization in UAV-Assisted Emergency Networks [71.01350912967922]
  In post-disaster scenarios, unmanned aerial vehicles (UAVs) are critical for establishing emergency communication networks.<n>This paper investigates age of information (AoI) minimization for UAV-assisted emergency communications with heterogeneous emergency services.<n>We develop MA-HEAD-Net, an adaptive rule-guided multi-agent deep reinforcement learning framework.
  arXiv  Detail & Related papers  (2026-08-02T10:05:50Z)
• HAD: Combining Hierarchical Diffusion with Metric-Decoupled RL for End-to-End Driving [51.268878540511054]
  We propose HAD, an end-to-end planning framework with a Hierarchical Diffusion Policy.<n>We show that HAD achieves new state-of-the-art performance on both NAVSIM and HUGSIM.
  arXiv  Detail & Related papers  (2026-04-04T04:12:47Z)
• Cluster-Aware Attention-Based Deep Reinforcement Learning for Pickup and Delivery Problems [6.977990610183958]
  emphCAADRL (Cluster-Aware Attention-based Deep Reinforcement Learning) is a DRL framework that exploits the multi-scale structure of PDP instances.<n>Our method achieves these results with substantially lower inference time than neural collaborative-search baselines.
  arXiv  Detail & Related papers  (2026-03-09T17:31:34Z)
• Learning in Context, Guided by Choice: A Reward-Free Paradigm for Reinforcement Learning with Transformers [55.33468902405567]
  We propose a new learning paradigm, In-Context Preference-based Reinforcement Learning (ICPRL), in which both pretraining and deployment rely solely on preference feedback.<n>ICPRL enables strong in-context generalization to unseen tasks, achieving performance comparable to ICRL methods trained with full reward supervision.
  arXiv  Detail & Related papers  (2026-02-09T03:42:16Z)
• Sample-Efficient Neurosymbolic Deep Reinforcement Learning [49.60927398960061]
  We propose a neuro-symbolic Deep RL approach that integrates background symbolic knowledge to improve sample efficiency.<n>Online reasoning is performed to guide the training process through two mechanisms.<n>We show improved performance over a state-of-the-art reward machine baseline.
  arXiv  Detail & Related papers  (2026-01-06T09:28:53Z)
• COVLM-RL: Critical Object-Oriented Reasoning for Autonomous Driving Using VLM-Guided Reinforcement Learning [55.83415345423854]
  We propose COVLM-RL, a novel end-to-end driving framework that integrates Critical Object-oriented (CO) reasoning with reinforcement learning (RL)<n>Experiments conducted in the CARLA simulator demonstrate that COVLM-RL significantly improves the success rate by 30% in trained driving environments.
  arXiv  Detail & Related papers  (2025-12-10T06:18:16Z)
• A Flexible Multi-Agent Deep Reinforcement Learning Framework for Dynamic Routing and Scheduling of Latency-Critical Services [18.675072317045466]
  Most existing network control solutions target only average delay performance, falling short of providing strict End-to-End (E2E) peak latency guarantees.<n>This paper addresses the challenge of reliably delivering packets within application-imposed deadlines by leveraging recent advancements in Multi-Agent Deep Reinforcement Learning (MA-DRL)<n>We present a novel MA-DRL network control framework that leverages a centralized routing and distributed scheduling architecture.
  arXiv  Detail & Related papers  (2025-10-13T15:38:10Z)
• Flow-Based Single-Step Completion for Efficient and Expressive Policy Learning [0.0]
  We propose a generative policy trained with an augmented flow-matching objective to predict direct completion vectors from intermediate flow samples.<n>Our method scales effectively to offline, offline-to-online, and online RL settings, offering substantial gains in speed and adaptability.<n>We extend SSCP to goal-conditioned RL, enabling flat policies to exploit subgoal structures without explicit hierarchical inference.
  arXiv  Detail & Related papers  (2025-06-26T16:09:53Z)
• Toward Dependency Dynamics in Multi-Agent Reinforcement Learning for Traffic Signal Control [8.312659530314937]
  Reinforcement learning (RL) emerges as a promising data-driven approach for adaptive traffic signal control.<n>In this paper, we propose a novel Dynamic Reinforcement Update Strategy for Deep Q-Network (DQN-DPUS)<n>We show that the proposed strategy can speed up the convergence rate without sacrificing optimal exploration.
  arXiv  Detail & Related papers  (2025-02-23T15:29:12Z)
• Closed-form congestion control via deep symbolic regression [1.5961908901525192]
  Reinforcement Learning (RL) algorithms can handle challenges in ultra-low-latency and high throughput scenarios. The adoption of neural network models in real deployments still poses some challenges regarding real-time inference and interpretability. This paper proposes a methodology to deal with such challenges while maintaining the performance and generalization capabilities.
  arXiv  Detail & Related papers  (2024-03-28T14:31:37Z)
• Efficient Parallel Split Learning over Resource-constrained Wireless Edge Networks [44.37047471448793]
  In this paper, we advocate the integration of edge computing paradigm and parallel split learning (PSL) We propose an innovative PSL framework, namely, efficient parallel split learning (EPSL) to accelerate model training. We show that the proposed EPSL framework significantly decreases the training latency needed to achieve a target accuracy.
  arXiv  Detail & Related papers  (2023-03-26T16:09:48Z)
• Semantic-Aware Collaborative Deep Reinforcement Learning Over Wireless Cellular Networks [82.02891936174221]
  Collaborative deep reinforcement learning (CDRL) algorithms in which multiple agents can coordinate over a wireless network is a promising approach. In this paper, a novel semantic-aware CDRL method is proposed to enable a group of untrained agents with semantically-linked DRL tasks to collaborate efficiently across a resource-constrained wireless cellular network.
  arXiv  Detail & Related papers  (2021-11-23T18:24:47Z)

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.