README.md

metadata

title: Overflow OpenENV
emoji: 🚗
colorFrom: blue
colorTo: green
sdk: gradio
sdk_version: 4.44.0
app_file: app.py
pinned: false
tags:
  - openenv

Overflow Environment

An autonomous vehicle fleet oversight environment for OpenEnv.

Overview

A 2D road grid with N cars. One car (Car 0) is controlled by an LLM agent, while other cars follow simple scripted driving rules. An observer detects crashes and near-misses each step and computes rewards based on safety.

Quick Start

# Install dependencies
pip install -e .

# Run the server
uvicorn server.app:app --host 0.0.0.0 --port 8000 --reload

from overflow_env import OverflowEnv, OverflowAction

async with OverflowEnv(base_url="http://localhost:8000") as env:
    result = await env.reset()
    print(result.observation.scene_description)

    action = OverflowAction(decision="maintain", reasoning="Road is clear ahead.")
    result = await env.step(action)
    print(result.observation.incident_report)
    print(f"Reward: {result.reward}, Done: {result.done}")

Action Space

Decision	Effect
accelerate	Increase speed by 5
brake	Decrease speed by 5
lane_change_left	Move to left lane
lane_change_right	Move to right lane
maintain	Keep current speed and lane

Reward Structure

Event	Reward
Crash (distance < 5)	-5.0
Near miss (distance < 15)	-1.0
Safe step toward goal	+0.5
Reached goal	+3.0
Reasoning quality bonus	+0.0 to +0.3

Environment Details

• Road: 3 lanes, ~200 units long
• Cars: 5 total (1 agent + 4 scripted)
• Max steps: 100 per episode
• Speed range: 20–90 units