Deep reinforcement learning-based control model for automatic robot navigation
This report explores the application of deep reinforcement learning (DRL) for robot navigation without pre-constructed maps. Several mainstream DRL models, including DDPG, PPO, and TD3, were tested in a simple static obstacle environment, and TD3 was found to have the best performance. The report th...
Main Author: | Deng, Haoyuan |
---|---|
Other Authors: | Jiang Xudong |
Format: | Final Year Project (FYP) |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/168320 |
Similar Items
-
Fuzzy reinforcement learning and its applications to mobile robot navigation
by: Deng, Chang
Published: (2008) -
Continuous control for robot based on deep reinforcement learning
by: Zhang, Shansi
Published: (2019) -
Mobile robot tracking control based on deep reinforcement learning
by: Toh, Yeong Jian
Published: (2021) -
Vision-based navigation and control of ground robots
by: Weng, Zhen
Published: (2022) -
Let the mobile robots learn to navigate in the crowd (WMX)
by: Tang, Longchen
Published: (2022)