Single Agent Robust Deep Reinforcement Learning for Bus Fleet Control

• URL: http://arxiv.org/abs/2508.20784v1
• Date: Thu, 28 Aug 2025 13:47:40 GMT
• Title: Single Agent Robust Deep Reinforcement Learning for Bus Fleet Control
• Authors: Yifan Zhang,
• Abstract summary: Bus bunching is a challenge for urban transit due to traffic and passenger demand.<n>We propose a novel single-agent reinforcement learning framework for bus holding control.<n>We show that our modified soft actor-critic achieves more stable and superior performance than benchmarks.
• Score: 9.910562011343009
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bus bunching remains a challenge for urban transit due to stochastic traffic and passenger demand. Traditional solutions rely on multi-agent reinforcement learning (MARL) in loop-line settings, which overlook realistic operations characterized by heterogeneous routes, timetables, fluctuating demand, and varying fleet sizes. We propose a novel single-agent reinforcement learning (RL) framework for bus holding control that avoids the data imbalance and convergence issues of MARL under near-realistic simulation. A bidirectional timetabled network with dynamic passenger demand is constructed. The key innovation is reformulating the multi-agent problem into a single-agent one by augmenting the state space with categorical identifiers (vehicle ID, station ID, time period) in addition to numerical features (headway, occupancy, velocity). This high-dimensional encoding enables single-agent policies to capture inter-agent dependencies, analogous to projecting non-separable inputs into a higher-dimensional space. We further design a structured reward function aligned with operational goals: instead of exponential penalties on headway deviations, a ridge-shaped reward balances uniform headways and schedule adherence. Experiments show that our modified soft actor-critic (SAC) achieves more stable and superior performance than benchmarks, including MADDPG (e.g., -430k vs. -530k under stochastic conditions). These results demonstrate that single-agent deep RL, when enhanced with categorical structuring and schedule-aware rewards, can effectively manage bus holding in non-loop, real-world contexts. This paradigm offers a robust, scalable alternative to MARL frameworks, particularly where agent-specific experiences are imbalanced.

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