README.md

RL4TurtleBot3Navigation

Use deep RL algorithms (SAC, DQN) to learn how turtlebot3 navigates in obstacle environments.

SAC agent navigates in non-obstacle environment:

DQN agents navigates in static obstacle environment:

Table of Contents

• Overview
• Requirements
• Training the Model

Overview

This repository is for my internship project which contains pytorch implementation (used stable baselines 3) to train a reinforcement learning navigation model. The robot is trained in Gazebo simulator.

1. Input

• Past and current Lidar: 24 samples from 0 to 359 degree
• Past and current velocity: angular and linear velocity.
• Current heading, distance to target: computed by using Robot Odometry.
• Lifetime: the time remaining to live (200 max timesteps per target).

Therefore, state dims = 2 * 24(lidar) + 2 * 1 (linear velocity) + 2 * 1(angular velocity) + 1(heading) + 1 (distance) + 1(lifetime) = 55 

2. Action:

• Discrete Action:
  • Fixed linear velocity (0.2 m/s):
  • Angular Velocity: 5 values: {-1.5, -0.75, 0, 0.75, 1.5} rad/s
• Continuous Action:
  • Linear velocity: [-1,1] m/s
  • Angular velocity: [-π/2,π/2] rad/s

3. Models

• DQN models:
• SAC models:

4. Rewards:

• Primary rewards:

  • Target reward
  • Collision reward

• Secondary rewards:

  • Heading & speed
  • Sharp turn
  • Continuity
  • Timeout

Requirements

1. Ubuntu 20.04 LTS.
2. Python 3.
3. ROS Neotic.
4. Gazebo simulator.
5. Some ROS packages: turtlebot3, openai_ros,...

Training the Model

First, run the stage simulator:

roslaunch turtlebot3_gazebo turtlebot3_stage_2.launch 

In a separate terminal, run the training code:

roslaunch my_turtlebot3_openai_example my_sb3_dqn_nav.launch