https://github.com/gabryv00/dqn_ros
Reinforcement Learning for Robot Navigation with ROS and Gazebo
https://github.com/gabryv00/dqn_ros
dqn gazebo reinforcement-learning ros turtlebot3
Last synced: 3 months ago
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Reinforcement Learning for Robot Navigation with ROS and Gazebo
- Host: GitHub
- URL: https://github.com/gabryv00/dqn_ros
- Owner: GabryV00
- Created: 2024-01-30T10:28:06.000Z (over 1 year ago)
- Default Branch: master
- Last Pushed: 2024-02-22T15:12:39.000Z (over 1 year ago)
- Last Synced: 2025-01-13T07:31:18.112Z (5 months ago)
- Topics: dqn, gazebo, reinforcement-learning, ros, turtlebot3
- Language: Python
- Homepage:
- Size: 9.12 MB
- Stars: 5
- Watchers: 2
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Robot Navigation with Reinforcement Learning with ROS and Gazebo
This repository contains the files for the execution of a Reinforcement Learning algorithm, i.e. the Deep Q-Learning algorithm, for the navigation of a robot, the TurtleBot3, in a simulated environment in Gazebo, using ROS.
## Architecture
The project consists of the following files:
- `src/my_turtlebot3_openai_example/scripts/start_deepqlearning.py` which is the main file that contains the details of the DQN architecture, all the calls to the OpenAI ROS library and the function for saving the data
- `src/my_turtlebot3_openai_example/config/my_turtlebot3_openai_deepqlearn_params.yaml` which contains all the parameters for the DQN architecture
- `src/openai_ros/src/openai_ros/task_envs/turtlebot3/config/turtlebot3_world.yaml` which contains parameters related to the TurtleBot3 enviroment
- `training_results/` contains the folder related to all the experiments conducted. Each folder contains:
* `plot.png` image containing the three plots relating to cumulative reward and total time, with the data not normalized
* `plot-std.png` image containing the three plots relating to cumulative reward and total time, with the normalized data
* `results-date.json` file containing all the parameters used in the experiment, plus the results obtained
## Documentation
The project contains also `RobotControl_slides.pdf` which is a presentation that describe that work done in this project