README.md

---
pretty_name: SimWorld Unreal Backend (Binary + Paks)
license: apache-2.0
language:
- en
tags:
- simulation
- unreal-engine
- binaries
- paks
- robotics
- multimodal
- agents
task_categories:
- other
size_categories:
- n<1K
---

# SimWorld: An Open-ended Realistic Simulator for Autonomous Agents in Physical and Social Worlds
<p align="center">
  <img src="https://github.com/user-attachments/assets/5d2da588-9470-44ef-82a9-5d45d592497a" width="840" height="795" alt="image" />
</p>


**SimWorld** is a simulation platform for developing and evaluating **LLM/VLM** AI agents in complex physical and social environments.

<div align="center">
    <a href="https://simworld-ai.github.io/"><img src="https://img.shields.io/badge/Website-SimWorld-blue" alt="Website" /></a>
    <a href="https://github.com/maitrix-org/SimWorld"><img src="https://img.shields.io/github/stars/maitrix-org/SimWorld?style=social" alt="GitHub Stars" /></a>
    <a href="https://simworld.readthedocs.io/en/latest"><img src="https://img.shields.io/badge/Documentation-Read%20Docs-green" alt="Documentation" /></a>
    <a href="https://arxiv.org/abs/2512.01078"><img src="https://img.shields.io/badge/arXiv-2512.01078-b31b1b?logo=arxiv&logoColor=white" alt="arXiv:2512.01078" /></a>
</div>

## 🔥 News
 - 2025.11 The white paper of **SimWorld** is available on arxiv!
 - 2025.9 **SimWorld** has been accepted to NeurIPS 2025 main track as a **spotlight** paper! 🎉
 - 2025.6 The first formal release of **SimWorld** has been published! 🚀
 - 2025.3 Our demo of **SimWorld** has been accepted by CVPR 2025 Demonstration Track! 🎉

## 💡 Introduction
SimWorld is built on Unreal Engine 5 and offers core capabilities to meet the needs of modern agent development. It provides:
- Realistic, open-ended world simulation with accurate physics and language-based procedural generation.
- Rich interface for LLM/VLM agents, supporting multi-modal perception and natural language actions.
- Diverse and customizable physical and social reasoning scenarios, enabling systematic training and evaluation of complex agent behaviors like navigation, planning, and strategic cooperation.

## 🏗️ Architecture
<p align="center">
    <img width="799" height="671" alt="image" src="https://github.com/user-attachments/assets/2e67356a-7dca-4eba-ab57-de1226e080bb" />
</p>

**SimWorld** consists of three layers:
- the Unreal Engine Backend, providing diverse and open-ended environments, rich assets and realistic physics simulation;
- the Environment layer, supporting procedural city generation, language-driven scene editing, gym-like APIs for LLM/VLM agents and traffic simulation;
- the Agent layer, enabling LLM/VLM agents to reason over multimodal observations and history while executing actions via a local action planner;

SimWorld's architecture is designed to be modular and flexible, supporting an array of functionalities such as dynamic world generation, agent control, and performance benchmarking. The components are seamlessly integrated to provide a robust platform for **Embodied AI** and **Agents** research and applications.

### Project Structure
```bash
simworld/               # Python package
    local_planner/      # Local action planner component
    agent/              # Agent system
    assets_rp/          # Live editor component for retrieval and re-placing
    citygen/            # City layout procedural generator
    communicator/       # Core component to connect Unreal Engine
    config/             # Configuration loader and default config file
    llm/                # Basic llm class
    map/                # Basic map class and waypoint system
    traffic/            # Traffic system
    utils/              # Utility functions
data/                   # Necessary input data
config/                 # Example configuration file and user configuration file
scripts/                # Examples of usage, such as layout generation and traffic simulation
docs/                   # Documentation source files
README.md
```

## Setup
### Installation
+ Python Client
Make sure to use Python 3.10 or later.
```bash
git clone https://github.com/SimWorld-AI/SimWorld.git
cd SimWorld
conda create -n simworld python=3.10
conda activate simworld
pip install -e .
```

+ UE server
Download the SimWorld server executable from S3:

We offer two versions of the SimWorld UE package: the base version, which comes with an empty map, and the additional environments version, which provides extra pre-defined environments for more diverse simulation scenarios. Both versions include all the core features of SimWorld.

| Platform | Package | Scenes/Maps Included | Download | Notes |
| --- | --- | --- | --- | --- |
| Windows | Base | Empty map for procedural generation | [Download (Base)](https://simworld-release.s3.us-east-1.amazonaws.com/SimWorld-Win64-v0_1_0-Foundation.zip) | Full agent features; smaller download. |
| Windows | Additional Environments | 100+ maps (including the empty one) | [Download (100+ Maps)](https://simworld-release.s3.us-east-1.amazonaws.com/SimWorld-Win64-v0_1_0-100Maps.zip) | Full agent features; larger download. |
| Linux | Base | Empty map for procedural generation | [Download (Base)](https://simworld-release.s3.us-east-1.amazonaws.com/SimWorld-Linux-v0_1_0-Foundation.zip) | Full agent features; smaller download. |
| Linux | Additional Environments | 100+ maps (including the empty one) | [Download (100+ Maps)](https://simworld-release.s3.us-east-1.amazonaws.com/SimWorld-Linux-v0_1_0-100Maps.zip) | Full agent features; larger download. |


**Note:**
1. Please check the [documentation](https://simworld.readthedocs.io/en/latest/getting_started/additional_environments.html#usage) for usage instructions of the **100+ Maps** version.
2. If you only need core functionality for development or testing, use **Base**. If you want richer demonstrations and more scenes, use the **Additional Environments (100+ Maps)**.

### Quick Start

We provide several examples of code in `examples/`, showcasing how to use the basic functionalities of SimWorld, including city layout generation, traffic simulation, asset retrieval, and activity-to-actions. Please follow the examples to see how SimWorld works.

#### Configuration

SimWorld uses YAML-formatted configuration files for system settings. The default configuration files are located in the `simworld/config` directory while user configurations are placed in the `config` directory.

- `simworld/config/default.yaml` serves as the default configuration file.
- `config/example.yaml` is provided as a template for custom configurations.

Users can switch between different configurations by specifying a custom configuration file path through the `Config` class:

To set up your own configuration:

1. Create your custom configuration by copying the example template:
   ```bash
   cp config/example.yaml config/your_config.yaml
   ```

2. Modify the configuration values in `your_config.yaml` according to your needs

3. Load your custom configuration in your code:
   ```python
   from simworld.config import Config
   config = Config('path/to/your_config')    # use absolute path here
   ```

#### Agent Action Space
SimWorld provides a comprehensive action space for pedestrians, vehicles and robots (e.g., move forward, sit down, pick up). For more details, see [actions](https://simworld.readthedocs.io/en/latest/components/ue_detail.html#actions) and `examples/ue_command.ipynb`.

#### Using the Camera
SimWorld supports a variety of sensors, including RGB images, segmentation maps, and depth images. For more details, please refer to the [sensors](https://simworld.readthedocs.io/en/latest/components/ue_detail.html#sensors) and the example script `examples/camera.ipynb`.

#### Commonly Used APIs
All APIs are located in `simworld/communicator`. Some of the most commonly used ones are listed below:
- `communicator.get_camera_observation`
- `communicator.spawn_object`
- `communicator.spawn_agent`
- `communicator.generate_world`
- `communicator.clear_env`

#### Simple Running Example

Once the SimWorld UE5 environment is running, you can connect from Python and control an in-world humanoid agent in just a few lines:
(The whole example of minimal demo is shown in `examples/gym_interface_demo.ipynb`)

```python
from simworld.communicator.unrealcv import UnrealCV
from simworld.communicator.communicator import Communicator
from simworld.agent.humanoid import Humanoid
from simworld.utils.vector import Vector
from simworld.llm.base_llm import BaseLLM
from simworld.local_planner.local_planner import LocalPlanner
from simworld.llm.a2a_llm import A2ALLM


# Connect to the running Unreal Engine instance via UnrealCV
ucv = UnrealCV()
comm = Communicator(ucv)

class Agent:
    def __init__(self, goal):
        self.goal = goal
        self.llm = BaseLLM("gpt-4o")
        self.system_prompt = f"You are an intelligent agent in a 3D world. Your goal is to: {self.goal}."

    def action(self, obs):
        prompt = f"{self.system_prompt}\n You are currently at: {obs}\nWhat is your next action?"
        action = self.llm.generate_text(system_prompt=self.system_prompt, user_prompt=prompt)
        return action

class Environment:
    def __init__(self, comm: Communicator):
        self.comm = comm
        self.agent: Humanoid | None = None
        self.action_planner = None
        self.agent_name: str | None = None
        self.target: Vector | None = None
        self.action_planner_llm = A2ALLM(model_name="gpt-4o-mini")

    def reset(self):
        """Clear the UE scene and (re)spawn the humanoid and target."""
        # Clear spawned objects
        self.comm.clear_env()

        # Blueprint path for the humanoid agent to spawn in the UE level
        agent_bp = "/Game/TrafficSystem/Pedestrian/Base_User_Agent.Base_User_Agent_C"

        # Initial spawn position and facing direction for the humanoid (2D)
        spawn_location, spawn_forward = Vector(0, 0), Vector(0, 1)
        self.agent = Humanoid(spawn_location, spawn_forward)
        self.action_planner = LocalPlanner(agent=self.agent, model=self.action_planner_llm, rule_based=False)

        # Spawn the humanoid agent in the Unreal world
        self.comm.spawn_agent(self.agent, name=None, model_path=agent_bp, type="humanoid")

        # Define a target position the agent is encouraged to move toward (example value)
        self.target = Vector(1000, 0)

        # Return initial observation (optional, but RL-style)
        observation = self.comm.get_camera_observation(self.agent.camera_id, "lit")

        return observation

    def step(self, action):
        """Use action planner to execute the given action."""
        # Parse the action text and map it to the action space
        primitive_actions = self.action_planner.parse(action)

        self.action_planner.execute(primitive_actions)

        # Get current location from UE (x, y, z) and convert to 2D Vector
        location = Vector(*self.comm.unrealcv.get_location(self.agent)[:2])

        # Camera observation for RL
        observation = self.comm.get_camera_observation(self.agent.camera_id, "lit")

        # Reward: negative Euclidean distance in 2D plane
        reward = -location.distance(self.target)

        return observation, reward


if __name__ == "__main__":
    # Create the environment wrapper
    agent = Agent(goal='Go to (1700, -1700) and pick up GEN_BP_Box_1_C.')
    env = Environment(comm)

    obs = env.reset()

    # Roll out a short trajectory
    for _ in range(100):
        action = agent.action(obs)
        obs, reward = env.step(action)
        print(f"obs: {obs}, reward: {reward}")
        # Plug this into your RL loop / logging as needed
```