8 citations · 20 across the 5 of their papers we have counts for
5 papers
Multi-Step First: A Lightweight Deep Reinforcement Learning Strategy for Robust Continuous Control with Partial Observability
Lingheng Meng, Rob Gorbet, Michael Burke +1
Deep Reinforcement Learning (DRL) has made considerable advances in simulated and physical robot control tasks, especially when problems admit a fully observed Markov Decision Proc…
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…
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…
Affective Movement Generation using Laban Effort and Shape and Hidden Markov Models
Ali Samadani, Rob Gorbet, Dana Kulic
Body movements are an important communication medium through which affective states can be discerned. Movements that convey affect can also give machines life-like attributes and h…
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…