land-following-with-reinforcement-learning/README.md at main · phuongboi/land-following-with-reinforcement-learning · GitHub

This repository is my re-implementation some experiments of the Master's thesis Training Neural Networks for Event-Based End-to-End Robot Control

[19/10/2023] Training with Q-learning

Using CoppeliaSim(V-REP), ROS, Q-learning
Simple and friendly implementation with pytorch
Modify the ROS interface with new V-REP version

CoppeliaSim simulation

Training result

Requirements

CoppeliaSim v4.5.1 linux
ROS Noetic, rospy
Pytorch

Setup

Launch roscore in one terminal before launch Coppeliasim in another terminal to make sure that CoppeliaSim can load ROS plugin properly
Open v_rep_scenario/scenario1.ttt in CoppeliaSim and modify child_script of Pioneer_p3dx by v_rep_scenario/rosInterfaceScript.lua
Start CoppeliaSim simulation, make sure topics is work as expect by rostopic list
Run python train_qnetwork.py

Reference

[1] https://github.com/clamesc/Training-Neural-Networks-for-Event-Based-End-to-End-Robot-Control
[2] Mnih, Volodymyr, et al. "Playing atari with deep reinforcement learning." arXiv preprint arXiv:1312.5602 (2013).