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 • 6 Dec 2022 • Cristian Millán-Arias, Ruben Contreras, Francisco Cruz, Bruno Fernandes
RL is a machine learning paradigm wherein an agent interacts with an environment to solve a given task.
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 • 8 Aug 2021 • Cristian Millán-Arias, Bruno Fernandes, Francisco Cruz
This behavior is an essential part of the communication process due to delimit the acceptable distance to interact with another being.
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).