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Triple-BERT: Do We Really Need MARL for Order Dispatch on Ride-Sharing Platforms?

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 6.5Download Report PDF
Reinforcement LearningOrder DispatchingRide Sharing
Abstract:

On-demand ride-sharing platforms, such as Uber and Lyft, face the intricate real-time challenge of bundling and matching passengers—each with distinct origins and destinations—to available vehicles, all while navigating significant system uncertainties. Due to the extensive observation space arising from the large number of drivers and orders, order dispatching, though fundamentally a centralized task, is often addressed using Multi-Agent Reinforcement Learning (MARL). However, independent MARL methods fail to capture global information and exhibit poor cooperation among workers, while Centralized Training Decentralized Execution (CTDE) MARL methods suffer from the curse of dimensionality. To overcome these challenges, we propose Triple-BERT, a centralized Single Agent Reinforcement Learning (MARL) method designed specifically for large-scale order dispatching on ride-sharing platforms. Built on a variant TD3, our approach addresses the vast action space through an action decomposition strategy that breaks down the joint action probability into individual driver action probabilities. To handle the extensive observation space, we introduce a novel BERT-based network, where parameter reuse mitigates parameter growth as the number of drivers and orders increases, and the attention mechanism effectively captures the complex relationships among the large pool of driver and orders. We validate our method using a real-world ride-hailing dataset from Manhattan. Triple-BERT achieves approximately an 11.95% improvement over current state-of-the-art methods, with a 4.26% increase in served orders and a 22.25% reduction in pickup times. Our code, trained model parameters, and processed data are publicly available at the anonymous repository https://anonymous.4open.science/r/Triple-BERT .

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes Triple-BERT, a centralized single-agent reinforcement learning framework for large-scale order dispatching on ride-sharing platforms. It resides in the 'Centralized Single-Agent RL Approaches' leaf, which contains only three papers total, including this work and two siblings. This represents a relatively sparse research direction within the broader taxonomy of fifty papers across the field, suggesting that centralized single-agent formulations remain less explored compared to the more populous multi-agent dispatching branch containing seven papers.

The taxonomy reveals that the paper's immediate neighbors include multi-agent RL dispatching frameworks, which dominate the core dispatching literature with seven papers addressing decentralized driver-order interactions. Adjacent branches explore context-aware dispatching methods, matching parameter optimization, and industry-deployed systems. Triple-BERT's centralized approach diverges from the multi-agent trend by treating dispatching as a unified decision problem rather than decomposing it into independent driver agents. The scope notes clarify that centralized methods emphasize global optimality while multi-agent approaches prioritize computational tractability through distributed control.

Among twenty-five candidates examined across three contributions, no clearly refuting prior work was identified. The core Triple-BERT framework examined ten candidates with zero refutations, the BERT-based architecture examined five candidates with zero refutations, and the action decomposition method examined ten candidates with zero refutations. This suggests that within the limited search scope of top-K semantic matches, the specific combination of centralized SARL with BERT-based state encoding and action decomposition appears relatively unexplored. However, the sibling papers in the same taxonomy leaf likely address overlapping challenges in centralized dispatching.

Based on the limited literature search of twenty-five candidates, the work appears to occupy a less-crowded methodological niche within centralized single-agent RL for ride-sharing. The analysis does not cover exhaustive prior work in deep learning architectures for sequential decision-making or action space decomposition techniques outside the ride-sharing domain. The novelty assessment reflects what was discoverable through semantic search and citation expansion, not a comprehensive field survey.

Taxonomy

Core-task Taxonomy Papers
50
3
Claimed Contributions
25
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: order dispatching on ride-sharing platforms using reinforcement learning. The field has evolved into a rich taxonomy spanning eight major branches. Core Dispatching and Matching Mechanisms address the fundamental problem of pairing riders with drivers, often through centralized single-agent RL approaches (e.g., Didi Dispatching[4], Context-Aware Taxi[7]) or distributed multi-agent frameworks (e.g., Distributed Ride-Sharing[20]). Vehicle Repositioning and Rebalancing focuses on proactive fleet management to anticipate demand imbalances (e.g., Lookahead Repositioning[18], Fleet Rebalancing[42]). Joint Optimization of Multiple Tasks tackles integrated decision-making across pricing, dispatching, and repositioning (e.g., Joint Pricing Dispatching[8], Joint Rebalancing Pricing[17]). Ride-Sharing and Pooling with Passenger Bundling explores efficient multi-passenger matching (e.g., AdaPool[24], Non-Myopic Pooling[40]). Autonomous and Mixed-Autonomy Fleet Management considers the operational challenges of self-driving fleets (e.g., Autonomous Ridesharing[10], Mixed Autonomy[9]). Fairness, Equity, and Multi-Objective Optimization emphasizes balancing platform efficiency with driver welfare and equity concerns (e.g., Long-Term Fairness[35], Fairness Micromobility[1]). Specialized Operational Contexts extend the core problem to electric vehicles, ultra-fast delivery, and other domains (e.g., Electric Fleet Operations[41], Ultra-Fast Delivery[3]). Finally, Surveys and Methodological Reviews synthesize the landscape (e.g., Demand-Driven Survey[23]). A particularly active line of work contrasts centralized versus distributed control: centralized methods often achieve global optimality but face scalability challenges, while distributed approaches trade coordination for computational tractability. Another key tension lies between myopic greedy matching and lookahead planning that accounts for future demand uncertainty. Triple-BERT[0] sits within the centralized single-agent RL branch, emphasizing sophisticated state representations for real-time dispatching decisions. Its focus on encoding rich contextual information aligns closely with Context-Aware Taxi[7] and Didi Dispatching[4], which similarly leverage deep learning to capture spatial-temporal patterns. Compared to Ultra-Fast Delivery[3], which adapts dispatching to tight time windows in logistics, Triple-BERT[0] addresses the classic ride-hailing setting with more flexible service constraints. The work exemplifies the ongoing effort to balance model expressiveness with the computational demands of large-scale urban operations.

Claimed Contributions

Triple-BERT centralized SARL framework for large-scale order dispatching

The authors propose Triple-BERT, a centralized Single-Agent Reinforcement Learning framework built on a variant of TD3 for order dispatching in ride-sharing platforms. This framework addresses large action spaces through action decomposition and tackles sample scarcity via a two-stage training method where feature extractors are first pre-trained using MARL.

10 retrieved papers
Novel BERT-based neural network architecture with QK-attention

The authors develop a novel network architecture based on BERT that uses self-attention to capture relationships between drivers and orders. The architecture incorporates a QK-attention module to reduce computational complexity and a positive normalization method to mitigate parameter redundancy issues.

5 retrieved papers
Action decomposition method for joint action probability

The authors introduce an action decomposition strategy that simplifies the joint action probability in the vast action space into individual action probabilities for each driver selecting each order, enabling independent driver decisions while maintaining global coordination.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Triple-BERT centralized SARL framework for large-scale order dispatching

The authors propose Triple-BERT, a centralized Single-Agent Reinforcement Learning framework built on a variant of TD3 for order dispatching in ride-sharing platforms. This framework addresses large action spaces through action decomposition and tackles sample scarcity via a two-stage training method where feature extractors are first pre-trained using MARL.

Contribution

Novel BERT-based neural network architecture with QK-attention

The authors develop a novel network architecture based on BERT that uses self-attention to capture relationships between drivers and orders. The architecture incorporates a QK-attention module to reduce computational complexity and a positive normalization method to mitigate parameter redundancy issues.

Contribution

Action decomposition method for joint action probability

The authors introduce an action decomposition strategy that simplifies the joint action probability in the vast action space into individual action probabilities for each driver selecting each order, enabling independent driver decisions while maintaining global coordination.

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