Mobile Robots Autonomous Exploration with Reinforcement Learning

Haojie Shi, LI ANG

Keywords: Q-Learning, Autonomous Exploration, Artificial Potential Field

TL;DR: Reinforcement Learning for Mobile Robots Autonomous Exploration

Abstract:

Reinforcement Learning, a computational approach to learning whereby an agent
tries to maximize the total amount of reward it receives while interacting with
a complex and uncertain environment. And it has quite a few applications in
playing game or controlling a robot after exploration and exploitation. Our project
is aimed at designing an efficient robot autonomous exploration algorithm based
on reinforcement learning. Our work is based on the HouseExpo dataset and
we developed reinforcement learning algorithm on it, showing that the robot can
efficiently explore uncertain environments with a smart behavior. Our work
is based on Double DQN with proportional prioritization[7], and we improve
the algorithm by encoding history observation, adding global explored map and
designing external reward via Artificial Potential Field.