45 citations · 96 across the 9 of their papers we have counts for
7 papers · 1 filter
Broad-persistent Advice for Interactive Reinforcement Learning Scenarios
Francisco Cruz, Adam Bignold, Hung Son Nguyen +2
The use of interactive advice in reinforcement learning scenarios allows for speeding up the learning process for autonomous agents. Current interactive reinforcement learning rese…
Explainable Reinforcement Learning for Broad-XAI: A Conceptual Framework and Survey
Richard Dazeley, Peter Vamplew, Francisco Cruz
Broad Explainable Artificial Intelligence moves away from interpreting individual decisions based on a single datum and aims to provide integrated explanations from multiple machin…
Learning Proxemic Behavior Using Reinforcement Learning with Cognitive Agents
Cristian Millán-Arias, Bruno Fernandes, Francisco Cruz
Proxemics is a branch of non-verbal communication concerned with studying the spatial behavior of people and animals. This behavior is an essential part of the communication proces…
Persistent Rule-based Interactive Reinforcement Learning
Adam Bignold, Francisco Cruz, Richard Dazeley +2
Interactive reinforcement learning has allowed speeding up the learning process in autonomous agents by including a human trainer providing extra information to the agent in real-t…
Deep Reinforcement Learning with Interactive Feedback in a Human-Robot Environment
Ithan Moreira, Javier Rivas, Francisco Cruz +3
Robots are extending their presence in domestic environments every day, being more common to see them carrying out tasks in home scenarios. In the future, robots are expected to in…
Improving interactive reinforcement learning: What makes a good teacher?
Francisco Cruz, Sven Magg, Yukie Nagai +1
Interactive reinforcement learning has become an important apprenticeship approach to speed up convergence in classic reinforcement learning problems. In this regard, a variant of…