A robot exploration strategy based on Q-learning network

By Lei Tai. 2016

152 citations recorded by the source corpus.

Subfields: Aerospace Engineering, Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Aerospace Engineering

1. Kaicheng Zhang
2. Farzad Niroui
3. Zendai Kashino
4. Goldie Nejat
5. Lei Tai

In Artificial Intelligence

1. Kaicheng Zhang
2. Goldie Nejat
3. Farzad Niroui
4. Zendai Kashino
5. Lei Tai

In Computer Vision and Pattern Recognition

1. Kaicheng Zhang
2. Farzad Niroui
3. Zendai Kashino
4. Goldie Nejat
5. Lei Tai
6. Ming Liu

Citations in the corpus

• Deep-learning in Mobile Robotics - from Perception to Control Systems: A Survey on Why and Why not (2016, methodology): “Most of the reinforcement learning applications in robotics are trained in simulation environments (Tai and Liu, 2016a; Zhang et al., 2016a).”
• Deep Reinforcement Learning Robot for Search and Rescue Applications: Exploration in Unknown Cluttered Environments (2019, methodology): “In [24], a DQN was used to learn robot movement directions (forward, right, left) using depth images as input to the network.”