MADRL-PosVCS

📄 Description

Indoor localization is drawing more and more attentions due to the growing demand of various location-based services, where fingerprinting is a popular data driven techniques that does not rely on complex measurement equipment, yet it requires site surveys which is both labor-intensive and time-consuming.

Vehicular crowdsensing (VCS) with unmanned vehicles (UVs) is a novel paradigm to navigate a group of UVs to collect sensory data from certain point-of-interests periodically (PoIs, i.e., coverage holes in localization scenarios).

In this paper, we formulate the multi-floor indoor fingerprint collection task with periodical PoI coverage requirements as a constrained optimization problem. Then, we propose a multi-agent deep reinforcement learning (MADRL) based solution, ``MADRL-PosVCS'', which consists of a primal-dual framework to transform the above optimization problem into the unconstrained duality, with adjustable Lagrangian multipliers to ensure periodic fingerprint collection. We also propose a novel intrinsic reward mechanism consists of the mutual information between a UV's observations and environment transition probability parameterized by a Bayesian Neural Network (BNN) for exploration, and a elevator-based reward to allow UVs to go cross different floors for collaborative fingerprint collections.

🔧 Installation

1. Clone repo

   git clone https://github.com/BIT-MCS/MADRL-PosVCS
   cd MADRL-PosVCS

2. Install dependent packages

   conda create --name PosVCS python==3.9
   conda activate PosVCS
   python -m pip install torch==1.12.1 torchvision==0.13.1 torchaudio==0.12.1 -f https://download.pytorch.org/whl/torch_stable.html
   pip install -r requirements.txt

   You might need to adjust your Torch version according to your GPU and operating system, or you can opt to use the CPU instead.

💻 Training

Train our solution

python ./MAPPO/train/train.py

🏁 Testing

Test with the trained models

python ./MAPPO/train/train.py --model_dir your_model_path --generate_outputs True

You can also view your trained trajectories through the command below, followed by the previous command.

python ./MAPPO/env_render.py

👏 code_Reference

• https://github.com/tinyzqh/light_mappo

📧 Contact

If you have any question, please email [email protected].