The retail industry faces increasing challenges in matching supply with demand due to evolving consumer behaviors, market volatility, and supply chain disruptions. While existing approaches employ statistical and machine learning methods for demand forecasting, they often fail to capture complex temporal dependencies and lack the ability to simultaneously optimize inventory decisions. This paper proposes a novel multi-agent deep reinforcement learning framework that jointly optimizes demand forecasting and inventory management in retail supply chains, leveraging data from IoT sensors, RFID tracking systems, and smart shelf monitoring devices. Our approach combines transformer-based sequence modeling for demand patterns with hierarchical reinforcement learning agents that coordinate inventory decisions across distribution networks. The framework integrates both historical sales data and real-time sensor measurements, employing attention mechanisms to capture seasonal patterns, promotional effects, and environmental conditions detected through temperature and humidity sensors. Through extensive experiments on large-scale retail datasets incorporating sensor network data, we demonstrate that our method achieves 18.2% lower forecast error and 23.5% reduced stockout rates compared with state-of-the-art baselines. The results show particular improvements in handling promotional events and seasonal transitions, where traditional methods often struggle. Our work provides new insights into leveraging deep reinforcement learning for integrated retail operations optimization and offers a scalable solution for modern sensor-enabled supply chain challenges.

Traditional marketing uplift models suffer from a fundamental limitation: they typically operate under static assumptions that fail to capture the temporal dynamics of customer responses to marketing interventions. This paper introduces a novel framework that combines causal forest algorithms with deep reinforcement learning to dynamically model marketing uplift effects. Our approach enables the real-time identification of heterogeneous treatment effects across customer segments while simultaneously optimizing intervention strategies through an adaptive learning mechanism. The key innovations of our framework include the following: (1) a counterfactual simulation environment that emulates diverse customer response patterns; (2) an adaptive reward mechanism that captures both immediate and long-term intervention outcomes; and (3) a dynamic policy optimization process that continually refines targeting strategies based on evolving customer behaviors. Empirical evaluations on both simulated and real-world marketing campaign data demonstrate that our approach significantly outperforms traditional static uplift models, achieving up to a 27% improvement in targeting efficiency and an 18% increase in the return on marketing investment. The framework leverages inherent symmetries in customer-intervention interactions, where balanced and symmetric reward structures ensure fair optimization across diverse customer segments. The proposed framework addresses the limitations of existing methods by effectively modeling the dynamic and heterogeneous nature of customer responses to marketing interventions, providing marketers with a powerful tool for implementing personalized and adaptive campaign strategies.

The widespread adoption of electric vehicles (EVs) necessitates advanced energy management strategies to alleviate range anxiety and improve overall energy efficiency. This study presents a novel framework for optimizing energy consumption in EVs by integrating driver behavior patterns, road conditions, and environmental factors. Utilizing a comprehensive dataset of 3395 high-resolution charging sessions from 85 EV drivers across 25 workplace locations, we developed a multi-modal prediction model that captures the complex interactions between driving behavior and environmental conditions. The proposed methodology employs a combination of driving scenario recognition and reinforcement learning techniques to optimize energy usage. Specifically, we utilize contrastive learning to extract meaningful representations of driving states by leveraging the symmetric relationships between positive pairs and the asymmetric nature of negative pairs and implement graph attention networks to model the intricate relationships between road environments and driving behaviors. Our experimental results demonstrate that the proposed framework achieves a significant reduction in energy consumption compared to baseline methods, with an average improvement of 17.3% in energy efficiency under various driving conditions. Furthermore, we introduce an adaptive real-time optimization strategy that dynamically adjusts vehicle parameters based on instantaneous driving patterns and environmental contexts. This research contributes to the advancement of intelligent energy management systems for EVs and provides insights into the development of more efficient and environmentally sustainable transportation solutions.