45 citations · 195 across the 15 of their papers we have counts for
17 papers
Introspection-based Explainable Reinforcement Learning in Episodic and Non-episodic Scenarios
Niclas Schroeter, Francisco Cruz, Stefan Wermter
With the increasing presence of robotic systems and human-robot environments in today's society, understanding the reasoning behind actions taken by a robot is becoming more import…
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…
Elastic Step DQN: A novel multi-step algorithm to alleviate overestimation in Deep QNetworks
Adrian Ly, Richard Dazeley, Peter Vamplew +2
Deep Q-Networks algorithm (DQN) was the first reinforcement learning algorithm using deep neural network to successfully surpass human level performance in a number of Atari learni…
Evaluating Human-like Explanations for Robot Actions in Reinforcement Learning Scenarios
Francisco Cruz, Charlotte Young, Richard Dazeley +1
Explainable artificial intelligence is a research field that tries to provide more transparency for autonomous intelligent systems. Explainability has been used, particularly in re…
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…
Explainable Deep Reinforcement Learning Using Introspection in a Non-episodic Task
Angel Ayala, Francisco Cruz, Bruno Fernandes +1
Explainable reinforcement learning allows artificial agents to explain their behavior in a human-like manner aiming at non-expert end-users. An efficient alternative of creating ex…
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…
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…
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…
Multi-modal Feedback for Affordance-driven Interactive Reinforcement Learning
Francisco Cruz, German I. Parisi, Stefan Wermter
Interactive reinforcement learning (IRL) extends traditional reinforcement learning (RL) by allowing an agent to interact with parent-like trainers during a task. In this paper, we…