7 citations · 11 across the 5 of their papers we have counts for
5 papers
Memory Allocation in Resource-Constrained Reinforcement Learning
Massimiliano Tamborski, David Abel
Resource constraints can fundamentally change both learning and decision-making. We explore how memory constraints influence an agent's performance when navigating unknown environm…
HAD-Gen: Human-like and Diverse Driving Behavior Modeling for Controllable Scenario Generation
Cheng Wang, Lingxin Kong, Massimiliano Tamborski +1
Simulation-based testing has emerged as an essential tool for verifying and validating autonomous vehicles (AVs). However, contemporary methodologies, such as deterministic and imi…
Deep Reinforcement Learning for Multi-Agent Interaction
Ibrahim H. Ahmed, Cillian Brewitt, Ignacio Carlucho +14
The development of autonomous agents which can interact with other agents to accomplish a given task is a core area of research in artificial intelligence and machine learning. Tow…
A Human-Centric Method for Generating Causal Explanations in Natural Language for Autonomous Vehicle Motion Planning
Balint Gyevnar, Massimiliano Tamborski, Cheng Wang +3
Inscrutable AI systems are difficult to trust, especially if they operate in safety-critical settings like autonomous driving. Therefore, there is a need to build transparent and q…
Verifiable Goal Recognition for Autonomous Driving with Occlusions
Cillian Brewitt, Massimiliano Tamborski, Cheng Wang +1
Goal recognition (GR) involves inferring the goals of other vehicles, such as a certain junction exit, which can enable more accurate prediction of their future behaviour. In auton…