---
title: "TerraZero：为零演示自博弈规模化而生的程序化驾驶模拟器"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2607.13028v1"
permalink: "https://feed.astralor.com/items/itm_0f00e678420bdf439fa1989b"
published: "2026-07-14T17:59:02.000Z"
score: 79
channel: "ai"
tags: ["TerraZero","强化学习","程序化驾驶模拟","自动驾驶","自博弈训练"]
---

# TerraZero：为零演示自博弈规模化而生的程序化驾驶模拟器

## AI 点评

> TerraZero 最令我兴奋的是它用纯自博弈在长尾基准上打败了所有有监督方法，这给‘数据匮乏’的极端场景带来了希望。对自动驾驶公司和研究者来说，这个模拟器既快又开源（如果开源的话）的话，可能会改变训练范式。

## 速览

TerraZero 是一个高效的程序化驾驶模拟器，专为零演示自博弈强化学习设计。其C引擎在CPU上运行仿真、GPU上运行策略推理，单服务器GPU可达每秒130万智能体步，远超现有模拟器。它仅利用真实地图几何数据，通过随机化规则来生成无限场景，无需人类演示即可从头训练策略。训练出的策略在InterPlan长尾基准上超越所有学习型规划器，成为首个登顶的完全学习型策略，在常规驾驶测试中也以最低碰撞率成为最安全方案，同时零样本泛化到左舵/右舵交通。

## 关键点

- TerraZero 在单GPU上达到每秒130万智能体步，训练效率极高
- 无需任何人类演示或专家数据，完全通过自博弈强化学习训练
- 在InterPlan长尾基准上成为首个登顶的完全学习型策略，安全指标最优
- 策略零样本泛化到不同城市和左舵/右舵交通规则

## 全文

Training robust autonomous driving agents requires a simulator that is fast enough for reinforcement learning at scale, realistic enough to ground behavior in real-world map structure, and diverse enough to cover the safety-critical long tail that logged data rarely contains. We present TerraZero, a procedural driving simulator and self-play training stack. A configurable C engine runs simulation on the CPU and policy inference on the GPU over a zero-copy path, sustaining 1.3M agent-steps per second on a single server-grade GPU, far faster than existing object-level simulators, while keeping fidelity lighter single-agent systems omit: heterogeneous agents, multiple dynamics models, and full traffic-rule enforcement. TerraZero treats logged data only as a source of real-world map geometry, populating each map with randomized rule-based road users and signal controllers and randomizing agent dynamics, rewards, and sizes per episode, so a map yields an unbounded set of scenarios. Every reported policy trains from scratch by reinforcement learning alone on a compute-efficient self-play recipe across GPUs, with zero human demonstrations and no fallback planner at inference. Policies generalize zero-shot across cities and datasets, including emergent left-hand-traffic driving without explicit supervision. As an ego policy, TerraZero is the first fully learned policy to top the InterPlan long-tail benchmark, ahead of larger learned planners; on routine-driving val14 it ranks among the best approaches and is the safest, posting the best collision and time-to-collision scores. On Waymo Open Sim Agents realism the same recipe outperforms other demonstration-free methods and is competitive with the strongest reference-anchored self-play method. One stack serves both roles: driving policies across dynamics for cars and trucks, and sim agents that jointly control vehicles, pedestrians, and cyclists.

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引用前请核对原文;本文由星际流动整理:https://feed.astralor.com/items/itm_0f00e678420bdf439fa1989b