5 papers
Integrating Counterfactual Simulations with Language Models for Explaining Multi-Agent Behaviour
Bálint Gyevnár, Christopher G. Lucas, Stefano V. Albrecht +1
Autonomous multi-agent systems (MAS) are useful for automating complex tasks but raise trust concerns due to risks such as miscoordination or goal misalignment. Explainability is v…
Studying the Interplay Between the Actor and Critic Representations in Reinforcement Learning
Samuel Garcin, Trevor McInroe, Pablo Samuel Castro +4
Extracting relevant information from a stream of high-dimensional observations is a central challenge for deep reinforcement learning agents. Actor-critic algorithms add further co…
People Attribute Purpose to Autonomous Vehicles When Explaining Their Behavior: Insights from Cognitive Science for Explainable AI
Balint Gyevnar, Stephanie Droop, Tadeg Quillien +4
It is often argued that effective human-centered explainable artificial intelligence (XAI) should resemble human reasoning. However, empirical investigations of how concepts from c…
DRED: Zero-Shot Transfer in Reinforcement Learning via Data-Regularised Environment Design
Samuel Garcin, James Doran, Shangmin Guo +2
Autonomous agents trained using deep reinforcement learning (RL) often lack the ability to successfully generalise to new environments, even when these environments share character…
How the level sampling process impacts zero-shot generalisation in deep reinforcement learning
Samuel Garcin, James Doran, Shangmin Guo +2
A key limitation preventing the wider adoption of autonomous agents trained via deep reinforcement learning (RL) is their limited ability to generalise to new environments, even wh…