447 citations · 905 across the 32 of their papers we have counts for
8 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…
Segmentation-Driven Feature-Preserving Mesh Denoising
Weijia Wang, Wei Pan, Chaofan Dai +4
Feature-preserving mesh denoising has received noticeable attention in visual media, with the aim of recovering high-fidelity, clean mesh shapes from the ones that are contaminated…
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…
Discrete-to-Deep Supervised Policy Learning
Budi Kurniawan, Peter Vamplew, Michael Papasimeon +2
Neural networks are effective function approximators, but hard to train in the reinforcement learning (RL) context mainly because samples are correlated. For years, scholars have g…
A Demonstration of Issues with Value-Based Multiobjective Reinforcement Learning Under Stochastic State Transitions
Peter Vamplew, Cameron Foale, Richard Dazeley
We report a previously unidentified issue with model-free, value-based approaches to multiobjective reinforcement learning in the context of environments with stochastic state tran…