447 citations · 697 across the 26 of their papers we have counts for
5 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 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…