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).