45 citations · 195 across the 15 of their papers we have counts for
7 papers · 1 filter
Human Engagement Providing Evaluative and Informative Advice for Interactive Reinforcement Learning
Adam Bignold, Francisco Cruz, Richard Dazeley +2
Interactive reinforcement learning proposes the use of externally-sourced information in order to speed up the learning process. When interacting with a learner agent, humans may p…
A Comparison of Humanoid Robot Simulators: A Quantitative Approach
Angel Ayala, Francisco Cruz, Diego Campos +3
Research on humanoid robotic systems involves a considerable amount of computational resources, not only for the involved design but also for its development and subsequent impleme…
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…
Moody Learners -- Explaining Competitive Behaviour of Reinforcement Learning Agents
Pablo Barros, Ana Tanevska, Francisco Cruz +1
Designing the decision-making processes of artificial agents that are involved in competitive interactions is a challenging task. In a competitive scenario, the agent does not only…
A Conceptual Framework for Externally-influenced Agents: An Assisted Reinforcement Learning Review
Adam Bignold, Francisco Cruz, Matthew E. Taylor +4
A long-term goal of reinforcement learning agents is to be able to perform tasks in complex real-world scenarios. The use of external information is one way of scaling agents to mo…
Explainable robotic systems: Understanding goal-driven actions in a reinforcement learning scenario
Francisco Cruz, Richard Dazeley, Peter Vamplew +1
Robotic systems are more present in our society everyday. In human-robot environments, it is crucial that end-users may correctly understand their robotic team-partners, in order t…
iCub: Learning Emotion Expressions using Human Reward
Nikhil Churamani, Francisco Cruz, Sascha Griffiths +1
The purpose of the present study is to learn emotion expression representations for artificial agents using reward shaping mechanisms. The approach takes inspiration from the TAMER…