saikirankannaiah436/obstacle-avoidance-in-airsim

A simple Reinforcement Learning Demo for Obstacle Avoidance of using Microsoft AirSim

A simple Reinforcement Learning Demo for Obstacle Avoidance of using Microsoft AirSim

This repository contains Python scripts showing how you can use Microsoft AirSim to collect image data from a moving vehicle, then use that data to train the vehicle to avoid obstacles in TensorFlow. The RL algorithm we used is D3QN(Double Deep Q Network with Dueling architecture)。

Prerequisites

• Recommended hardware for running UnrealEngine4, required for AirSim. Although it is possible build AirSim on OS X and Linux, we found it easiest to use the pre-compiled Windows binaries.
• This map we show aboved is a simple demo,which was built on Block.
• Python3 for 64-bit Windows
• TensorFlow. To run TensorFlow on your GPU as we and most people do, you'll need to follow the directions for installing CUDA and CuDNN. We recommend setting aside at least an hour to make sure you do this right.

Document

• D3QN_training_standard.py
  

  This script is a standard Monocular-Obstacle-Avoidance training program. With only a monocular, the moving vehicle can learning to avoid obstacles.

• D3QN_training_Lidar.py
  

  With a monocular and lidar, the moving vehicle can learning to avoid obstacles more efficiently.

• D3QN_testing_Lidar_grid.py
  

  When the vehicle is well trained, you can run this test program. When running, the car records the explored space in a grid map simultaneously, which will be saved as .pkl file in the same path.

Instructions

1. Clone this repository.

2. Open or build a map, set the SimMode:"Car" in the setting.json, and then run it.

3. Choose a train model and modified the destination coordinate, then run

   python 3QN_training_standard.py or python D3QN_training_Lidar.py
   

   It will take a long time. if you choose training your car with lidar, it will be more efficient.

4. when you find the moving vehicle trained well, then run

   python D3QN_testing_Lidar_grid.py
   

   The car is testing without lidar for the lidar is just a auxiliary tools in training task

5. It's a simple demo for Obstacle Avoidance with D3QN, you can change the structure with DDPG or A3C quite easily.

show our result

The average steps in a episode

methods	Steps
D3QN training	850
D3QN+Lidar training	3200
Test without Lidar	2185

Note: When the moving vehicle reached the destination or has collided, a episode is over.

Acknowledgement

This code repository is highly inspired from work of Linhai Xie, Sen Wang, Niki trigoni, Andrew Markham

[[link]]: https://github.com/xie9187/Monocular-Obstacle-Avoidance