8 citations · 8 across the 1 of their papers we have counts for
3 papers
cs.LG2021
Memory-based Deep Reinforcement Learning for POMDPs
Lingheng Meng, Rob Gorbet, Dana Kulić
A promising characteristic of Deep Reinforcement Learning (DRL) is its capability to learn optimal policy in an end-to-end manner without relying on feature engineering. However, m…
cs.AI2020★ 8 cited
The Effect of Multi-step Methods on Overestimation in Deep Reinforcement Learning
Lingheng Meng, Rob Gorbet, Dana Kulić
Multi-step (also called n-step) methods in reinforcement learning (RL) have been shown to be more efficient than the 1-step method due to faster propagation of the reward signal, b…
cs.HC2019
Learning to Engage with Interactive Systems: A Field Study on Deep Reinforcement Learning in a Public Museum
Lingheng Meng, Daiwei Lin, Adam Francey +3
Physical agents that can autonomously generate engaging, life-like behaviour will lead to more responsive and interesting robots and other autonomous systems. Although many advance…