OpComm: A Reinforcement Learning Framework for Adaptive Buffer Control in Warehouse Volume Forecasting

• URL: http://arxiv.org/abs/2512.19738v1
• Date: Wed, 17 Dec 2025 17:21:19 GMT
• Title: OpComm: A Reinforcement Learning Framework for Adaptive Buffer Control in Warehouse Volume Forecasting
• Abstract summary: OpComm is a forecasting and decision-support framework that combines supervised learning with reinforcement learning-based buffer control.<n>Across 400+ stations, OpComm reduced weighted Absolute Percentage Error (WAPE) by 21.65% compared to manual forecasts.
• Score: 28.715625330574966
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate forecasting of package volumes at delivery stations is critical for last-mile logistics, where errors lead to inefficient resource allocation, higher costs, and delivery delays. We propose OpComm, a forecasting and decision-support framework that combines supervised learning with reinforcement learning-based buffer control and a generative AI-driven communication module. A LightGBM regression model generates station-level demand forecasts, which serve as context for a Proximal Policy Optimization (PPO) agent that selects buffer levels from a discrete action set. The reward function penalizes under-buffering more heavily than over-buffering, reflecting real-world trade-offs between unmet demand risks and resource inefficiency. Station outcomes are fed back through a Monte Carlo update mechanism, enabling continual policy adaptation. To enhance interpretability, a generative AI layer produces executive-level summaries and scenario analyses grounded in SHAP-based feature attributions. Across 400+ stations, OpComm reduced Weighted Absolute Percentage Error (WAPE) by 21.65% compared to manual forecasts, while lowering under-buffering incidents and improving transparency for decision-makers. This work shows how contextual reinforcement learning, coupled with predictive modeling, can address operational forecasting challenges and bridge statistical rigor with practical decision-making in high-stakes logistics environments.

Related papers

• Goal-oriented probabilistic forecasting for dynamic PRB allocation in 5G networks [3.0732546238367338]
  We propose a goal-oriented probabilistic forecasting framework that aligns model training with the operator's decision-making objectives.<n> Evaluation on a real beam-level 5G traffic dataset shows that the proposed approach reduces operational cost compared to MSE-trained baselines.
  arXiv  Detail & Related papers  (2026-09-15T15:08:36Z)
• 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)
• ADORN: Adaptive Drift handling for Open RAN using Reinforcement Learning [0.9577207941409713]
  Dynamic traffic variations in Open Radio Access Networks (O-RAN) lead to drift.<n>Traditional retraining approaches maintain forecasting accuracy but incur high computational cost.<n>This work proposes a Q-learning-based adaptive retraining approach that formulates the retraining decision as a Markov Decision Process.
  arXiv  Detail & Related papers  (2026-07-09T13:05:42Z)
• Diffusion Controller: Framework, Algorithms and Parameterization [54.82539154511621]
  We present a unified control-theoretic view that casts reverse diffusion sampling as state-only control within (generalized) linearly-solvable Markov Decision Processes.<n>Under this framework, control acts by reweighting the pretrained reverse-time transition kernels, balancing terminal objectives against an $f$-divergence cost.<n>Experiments on Stable Diffusion v1.4 show consistent gains in preference-alignment win rates and improved quality-efficiency trade-offs.
  arXiv  Detail & Related papers  (2026-03-07T01:49:59Z)
• Unbiased Dynamic Pruning for Efficient Group-Based Policy Optimization [60.87651283510059]
  Group Relative Policy Optimization (GRPO) effectively scales LLM reasoning but incurs prohibitive computational costs.<n>We propose Dynamic Pruning Policy Optimization (DPPO), a framework that enables dynamic pruning while preserving unbiased gradient estimation.<n>To mitigate the data sparsity induced by pruning, we introduce Dense Prompt Packing, a window-based greedy strategy.
  arXiv  Detail & Related papers  (2026-03-04T14:48:53Z)
• DFPO: Scaling Value Modeling via Distributional Flow towards Robust and Generalizable LLM Post-Training [94.568675548967]
  Training reinforcement learning (RL) systems in real-world environments remains challenging due to noisy supervision and poor out-of-domain generalization.<n>Recent distributional RL methods improve robustness by modeling values with multiple quantile points, but they still learn each quantile independently as a scalar.<n>We propose DFPO, a robust distributional RL framework that models values as continuous flows across time steps.
  arXiv  Detail & Related papers  (2026-02-05T17:07:42Z)
• Rethinking the Trust Region in LLM Reinforcement Learning [72.25890308541334]
  Proximal Policy Optimization (PPO) serves as the de facto standard algorithm for Large Language Models (LLMs)<n>We propose Divergence Proximal Policy Optimization (DPPO), which substitutes clipping with a more principled constraint.<n>DPPO achieves superior training and efficiency compared to existing methods, offering a more robust foundation for RL-based fine-tuning.
  arXiv  Detail & Related papers  (2026-02-04T18:59:04Z)
• Data-Driven Stochastic VRP: Integration of Forecast Duration into Optimization for Utility Workforce Management [0.913755431537592]
  We exploit tree-based boosting gradient (XGBoost) trained on eight years of gas meter maintenance data to produce point predictions and uncertainty estimates.<n>Our results report improvements around 20-25% in operator completion rates compared with plans computed using default durations.
  arXiv  Detail & Related papers  (2026-01-12T13:12:46Z)
• BAPO: Stabilizing Off-Policy Reinforcement Learning for LLMs via Balanced Policy Optimization with Adaptive Clipping [69.74252624161652]
  We propose BAlanced Policy Optimization with Adaptive Clipping (BAPO)<n>BAPO dynamically adjusts clipping bounds to adaptively re-balance positive and negative contributions, preserve entropy, and stabilize RL optimization.<n>On AIME 2024 and AIME 2025 benchmarks, our 7B BAPO model surpasses open-source counterparts such as SkyWork-OR1-7B.
  arXiv  Detail & Related papers  (2025-10-21T12:55:04Z)
• Reinforcement Fine-Tuning of Flow-Matching Policies for Vision-Language-Action Models [7.316631310935769]
  Vision-Language-Action (VLA) models have shown strong generalization by leveraging large-scale demonstrations.<n>We propose Flow Policy Optimization (FPO) algorithm, which reformulates importance sampling by leveraging per-sample changes in the conditional flow-matching objective.<n>We evaluate FPO on the LIBERO benchmark and the ALOHA simulation task against supervised, preference-aligned, diffusion-based, autoregressive online RL.
  arXiv  Detail & Related papers  (2025-10-11T03:11:18Z)
• Reinforce-Ada: An Adaptive Sampling Framework for Reinforce-Style LLM Training [47.26632817047513]
  Reinforcement learning applied to large language models (LLMs) for reasoning tasks is often bottlenecked by unstable gradient estimates.<n>We propose Reinforce-Ada, an adaptive sampling framework for online RL post-training of LLMs.<n>Unlike conventional two-stage allocation methods, Reinforce-Ada interleaves estimation and sampling in an online successive elimination process.
  arXiv  Detail & Related papers  (2025-10-06T16:34:09Z)
• Beamforming and Resource Allocation for Delay Minimization in RIS-Assisted OFDM Systems [38.71413228444903]
  This paper investigates a joint beamforming and resource allocation problem in downlink reconfigurable intelligent surface (RIS)-assisted OFDM systems.<n>To effectively handle the mixed action space and reduce the state space dimensionality, a hybrid deep reinforcement learning (DRL) approach is proposed.<n>The proposed algorithm significantly reduces the average delay, enhances resource allocation efficiency, and achieves superior system robustness and fairness.
  arXiv  Detail & Related papers  (2025-06-04T05:33:33Z)
• Efficient Safety Alignment of Large Language Models via Preference Re-ranking and Representation-based Reward Modeling [84.00480999255628]
  Reinforcement Learning algorithms for safety alignment of Large Language Models (LLMs) encounter the challenge of distribution shift.<n>Current approaches typically address this issue through online sampling from the target policy.<n>We propose a new framework that leverages the model's intrinsic safety judgment capability to extract reward signals.
  arXiv  Detail & Related papers  (2025-03-13T06:40:34Z)
• OptScaler: A Collaborative Framework for Robust Autoscaling in the Cloud [10.97507717758812]
  We propose OptScaler, a collaborative autoscaling framework that integrates proactive and reactive modules through an optimization module.<n> Numerical results have demonstrated the superiority of our workload prediction model and the collaborative framework.
  arXiv  Detail & Related papers  (2023-10-26T04:38:48Z)
• Compressed Regression over Adaptive Networks [58.79251288443156]
  We derive the performance achievable by a network of distributed agents that solve, adaptively and in the presence of communication constraints, a regression problem. We devise an optimized allocation strategy where the parameters necessary for the optimization can be learned online by the agents.
  arXiv  Detail & Related papers  (2023-04-07T13:41:08Z)

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.