MetaLore is a simulation environment for exploring dynamic resource allocation in smart city and metaverse-applications. It models the interaction between mobile users, sensors, base stations and edge servers in a sub-metaverse, where efficient orchestration of limited communication and computational resources is critical for real-time synchronization between the physical and digital worlds.
Built upon the mobile-env framework, MetaLore extends its capabilities to support deep reinforcement learning (DRL)-based control and Age of Information (AoI)-aware optimization.
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Joint Resource Allocation:
Dynamically splits both communication bandwidth and computational resources between mobile user equipment (UEs) and stationary environmental sensors. -
Custom AoI Metrics:
Integrates novel delay-aware metrics:- AoRI (Age of Request Information) — measures end-to-end service latency.
- AoSI (Age of Sensor Information) — captures data freshness from sensors.
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Multi-Objective Optimization:
Balances throughput, latency and synchronization accuracy through a DRL reward function. -
DRL Integration:
Fully compatible with Gymnasium and Stable-Baselines3, with native support for Proximal Policy Optimization (PPO). -
Visualizations & Evaluation Tools:
Built-in tools for monitoring queue dynamics, AoI metrics and policy performance.
MetaLore was developed as part of an ongoing PhD research project focused on real-time synchronization and orchestration in smart city environments. Using reinforcement learning, the system learns to adaptively manage heterogeneous traffic demands and maintain digital twin synchronization in dynamic network conditions.
For development, you can clone Metalore from GitHub and install it from source.
After cloning, install in "editable" mode (-e):
pip install -e .This is functionally equivalent to installing the dependencies from requirements.txt.
import gymnasium
import mobile_env
env = gymnasium.make("mobile-smart_city-smart_city_handler-v0")
obs, info = env.reset()
done = False
while not done:
action = ...
obs, reward, terminated, truncated, info = env.step(action)
done = terminated or truncated
env.render()Full API documentation and usage examples are under development and will be released soon.
We welcome any contributions to the MetaLore Simulator. It can be adding new features, refining existing functionalities, resolving bugs or improving documentation.
If you use MetaLore simulator in your work, please cite our paper: coming soon!
1. Fork the Repository: Start by creating a fork of this repository to your GitHub account. 2. Create a Feature Branch: Work on your changes in a dedicated feature branch to keep development organized. 3. Submit a Pull Request (PR): Once your changes are ready, submit a PR describing the enhancement, fix or addition.
We value well-documented and tested contributions that align with the project's goals and coding standards.
If you use MetaLore Simulator in your research, please let us know and we will feature your project. For any questions, feedback or ideas feel free to open an issue.
MetaLore is a collaborative project between the LIP6 lab at Sorbonne University and Nokia Germany.
This project was developed using the mobile-env codebase. We extend our gratitude to the mobile-env team for their foundational work in mobile network simulation, which served as an important starting point for this project.
If you'd like to reference the original work, please see their paper in PDF.
For more information on mobile-env, visit their GitHub repository.
For questions or further information, please feel free to contact elif-ebru.ohri@lip6.fr or open an issue on this repository.
This project is licensed under the MIT License.
- S. Schneider, S. Werner, R. Khalili, A. Hecker, and H. Karl, “mobile-env: An open platform for reinforcement learning in wireless mobile networks,” in Network Operations and Management Symposium (NOMS). IEEE/IFIP, 2022.