4 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…
An Overview + Detail Layout for Visualizing Compound Graphs
Chang Han, Justin Lieffers, Clayton Morrison +1
Compound graphs are networks in which vertices can be grouped into larger subsets, with these subsets capable of further grouping, resulting in a nesting that can be many levels de…
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,…
Using Features at Multiple Temporal and Spatial Resolutions to Predict Human Behavior in Real Time
Liang Zhang, Justin Lieffers, Adarsh Pyarelal
When performing complex tasks, humans naturally reason at multiple temporal and spatial resolutions simultaneously. We contend that for an artificially intelligent agent to effecti…