zerosansan/dqn_qlearning_sarsa_mobile_robot_navigation
A Reinforcement Learning (RL) based navigation implementation for mobile robot navigation. Q-Learning, SARSA and Deep Q-Network algorithms were compared.
This helps robotics researchers and engineers train mobile robots to navigate autonomously using reinforcement learning. You can input various learning algorithms (Q-Learning, SARSA, DQN) and simulated or real-world sensor data from a Turtlebot3 robot. The output is a trained robot capable of independent navigation within an environment. This is for professionals working on autonomous robot development.
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Use this if you are developing or experimenting with mobile robot navigation strategies and want to compare different reinforcement learning approaches in simulation and on a physical robot.
Not ideal if you need to control non-Turtlebot3 robot platforms or are looking for ready-to-deploy, off-the-shelf navigation solutions without customization.
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40
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Language
Python
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Last pushed
Sep 25, 2022
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