no code implementations • 14 Dec 2022 • Hugo Muñoz, Ernesto Portugal, Angel Ayala, Bruno Fernandes, Francisco Cruz
The results obtained showed that it is possible to use the memory-based method in hierarchical environments with high-level tasks and compute the probabilities of success to be used as a basis for explaining the agent's behavior.
no code implementations • 18 Aug 2021 • Angel Ayala, Francisco Cruz, Bruno Fernandes, Richard Dazeley
Explainable reinforcement learning allows artificial agents to explain their behavior in a human-like manner aiming at non-expert end-users.
no code implementations • 9 Sep 2020 • Ruben Contreras, Angel Ayala, Francisco Cruz
The obtained results show that the unmanned aerial vehicle is capable of interpreting user voice instructions achieving an improvement in speech-to-action recognition for both languages when using phoneme matching in comparison to only using the cloud-based algorithm without domain-based instructions.
1 code implementation • 16 Aug 2020 • Angel Ayala, Bruno Fernandes, Francisco Cruz, David Macêdo, Adriano L. I. Oliveira, Cleber Zanchettin
The experiments show that our model keeps high accuracy while substantially reducing the number of parameters and flops.
no code implementations • 7 Jul 2020 • Ithan Moreira, Javier Rivas, Francisco Cruz, Richard Dazeley, Angel Ayala, Bruno Fernandes
We compare three different learning methods using a simulated robotic arm for the task of organizing different objects; the proposed methods are (i) deep reinforcement learning (DeepRL); (ii) interactive deep reinforcement learning using a previously trained artificial agent as an advisor (agent-IDeepRL); and (iii) interactive deep reinforcement learning using a human advisor (human-IDeepRL).