45 citations · 96 across the 9 of their papers we have counts for
13 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…
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…