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DeliveryGym: An RL Environment for Long-Horizon Embodied Agent Planning with Adaptive Curriculum

arXiv · AI, language, vision and robotics · article · Sep 17, 2026 · UTC

Executable environments enable LLM agents to learn from the consequences of their actions. For embodied agents, those consequences extend beyond whether the current task succeeds: completing a delivery can consume the time, energy, or money needed for later work. Learning to plan therefore requires environments that preserve these dependencies and turn them into feedback across a complete trajectory. We introduce DeliveryGym, a 3D environment for evaluating and training agents on continuous courier shifts. It couples multimodal tool interaction with persistent world dynamics and computes traje

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