Research map / Multi-agent systems and games / Human-AI and goal-conditioned
Nocturne: a scalable driving benchmark for bringing multi-agent learning one step closer to the real world
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| add target speed to goal specification (ablation) | APPO · Nocturne test scenes | collision rate (%): 29.3 -> 20.3 | Helped |
| add target speed to goal specification (ablation) | APPO · Nocturne test scenes | goal rate (%): 64.9 -> 71.7 | Helped |
| add target speed to goal specification (ablation) | APPO · Nocturne test scenes | FDE (m): 12.6 -> 6.1 | Helped |
| train on 10000 scenes instead of fewer files (ablation) | APPO · Nocturne | train-test gap | Helped |
| train on up to 1000 files instead of fewer (ablation) | APPO · Nocturne training set | goal rate | Hurt |
Closest papers by meaning
- DriveAgent-R1: Advancing VLM-based Autonomous Driving with Active Perception and Hybrid Thinking (ICLR 2026 · similarity 0.86)
- MAPLE: Latent Multi-Agent Play for End-to-End Autonomous Driving (NeurIPS 2026 · similarity 0.86)
- IntersectionZoo: Eco-driving for Benchmarking Multi-Agent Contextual Reinforcement Learning (ICLR 2025 · similarity 0.86)
- UniMotion: A Unified Motion Framework for Simulation, Prediction and Planning (NeurIPS 2025 · similarity 0.85)
- HASARD: A Benchmark for Vision-Based Safe Reinforcement Learning in Embodied Agents (ICLR 2025 · similarity 0.85)
- Agent-X: Evaluating Deep Multimodal Reasoning in Vision-Centric Agentic Tasks (ICLR 2026 · similarity 0.85)
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