https://github.com/odegnome/dcode
Implementation of Deep Reinforcement Learning for Collision Prevention in Quadrotor
https://github.com/odegnome/dcode
autonomous-agents autonomous-quadcoptor dissertation-project machine-learning python3 quadrotor reinforcement-learning
Last synced: 5 months ago
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Implementation of Deep Reinforcement Learning for Collision Prevention in Quadrotor
- Host: GitHub
- URL: https://github.com/odegnome/dcode
- Owner: odegnome
- Created: 2021-12-24T17:34:39.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2025-01-24T10:20:24.000Z (over 1 year ago)
- Last Synced: 2025-06-07T05:07:13.224Z (about 1 year ago)
- Topics: autonomous-agents, autonomous-quadcoptor, dissertation-project, machine-learning, python3, quadrotor, reinforcement-learning
- Language: Jupyter Notebook
- Homepage:
- Size: 5.15 MB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Implementation of Deep Reinforcement Learning for Collision Prevention in Quadrotor
In this project, DRL was used to train an autonomous quadrotor to be able to fly in a
closed space, eg, a room. However, the trained policy did not perform well but the reasoning
is already mentioned in the dissertation report. Moreover, as a follow up, I used preimplemented
algorithms, in case my implementation was wrong, and also changed the reward mechanism. This
changed the performance of the quadrotor, as it was able to sustain a flight within the
constrained space, without colliding. The code is yet to be uploaded, but will update here.
This repo contains code and dissertation report from my Master's course. Code relevant
to training the policy and testing the performance has been added, but some irrelevant
part has been omitted. However, should someone feel that the code in incomplete, please
raise an [Issue](https://github.com/odegnome/dcode/issues) and I'll see what I can do.
The dissertation report should also be available in the same repo.
Good luck.