- 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, and we improve the algorithm by encoding history observation, adding global explored map and designing external reward via Artificial Potential Field.