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Anwar, Malik Aqeel
2019-10-16 16:58:14 -04:00
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## What is DRLwithTL-Sim?
This repository uses Transfer Learning (TL) based approach to reduce on-board computation required to train a deep neural network for autonomous navigation via Deep Reinforcement Learning for a target algorithmic performance. A library of 3D realistic meta-environments is manually designed using Unreal Gaming Engine and the network is trained end-to- end. These trained meta-weights are then used as initializers to the network in a **simulated** test environment and fine-tuned for the last few fully connected layers. Variation in drone dynamics and environmental characteristics is carried out to show robustness of the approach.
The repository containing the code for **real** environment on a **real** DJI Tello drone can be found @ [DRLwithTL-Real](www.google.com)
The repository containing the code for **real** environment on a **real** DJI Tello drone can be found @ [DRLwithTL-Real](https://github.com/aqeelanwar/DRLwithTL_real)
## Installing DRLwithTL-Sim
The current version of DRLwithTL-Sim supports Windows and requires python3. It’s advisable to [make a new virtual environment](https://towardsdatascience.com/setting-up-python-platform-for-machine-learning-projects-cfd85682c54b) for this project and install the dependencies. Following steps can be taken to download get started with DRLwithTL-Sim
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### Create/Download a simulated environment
You can either manually create your environment using Unreal Engine, or can download one of the sample environments from the link below and run it.
* [Indoor Long Environment](https://www.google.com)
* [Indoor Long Environment](https://drive.google.com/file/d/1yfFaI_9yXNa9iuLBbOtCfzoUOOV0iVoL/view?usp=sharing)
The link above will download the packaged version of the _Indoor Long environment_. Run the indoor_long.exe file to run the environment.