13 citations · 28 across the 5 of their papers we have counts for
5 papers
Enhancing Interpretability in Deep Reinforcement Learning through Semantic Clustering
Liang Zhang, Justin Lieffers, Adarsh Pyarelal
In this paper, we explore semantic clustering properties of deep reinforcement learning (DRL) to improve its interpretability and deepen our understanding of its internal semantic…
Deep Reinforcement Learning with Vector Quantized Encoding
Liang Zhang, Justin Lieffers, Adarsh Pyarelal
Human decision-making often involves combining similar states into categories and reasoning at the level of the categories rather than the actual states. Guided by this intuition,…
Leveraging Queue Length and Attention Mechanisms for Enhanced Traffic Signal Control Optimization
Liang Zhang, Shubin Xie, Jianming Deng
Reinforcement learning (RL) techniques for traffic signal control (TSC) have gained increasing popularity in recent years. However, most existing RL-based TSC methods tend to focus…
Efficient Pressure: Improving efficiency for signalized intersections
Qiang Wu, Liang Zhang, Jun Shen +3
Since conventional approaches could not adapt to dynamic traffic conditions, reinforcement learning (RL) has attracted more attention to help solve the traffic signal control (TSC)…
Expression might be enough: representing pressure and demand for reinforcement learning based traffic signal control
Liang Zhang, Qiang Wu, Jun Shen +3
Many studies confirmed that a proper traffic state representation is more important than complex algorithms for the classical traffic signal control (TSC) problem. In this paper, w…