1 citations · 1 across the 3 of their papers we have counts for
5 papers
Multi-Bellman operator for convergence of -learning with linear function approximation
Diogo S. Carvalho, Pedro A. Santos, Francisco S. Melo
We study the convergence of -learning with linear function approximation. Our key contribution is the introduction of a novel multi-Bellman operator that extends the traditional…
Centralized Training with Hybrid Execution in Multi-Agent Reinforcement Learning
Pedro P. Santos, Diogo S. Carvalho, Miguel Vasco +4
We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully complete cooperative tasks with arbitrary communicati…
Hierarchically Structured Scheduling and Execution of Tasks in a Multi-Agent Environment
Diogo S. Carvalho, Biswa Sengupta
In a warehouse environment, tasks appear dynamically. Consequently, a task management system that matches them with the workforce too early (e.g., weeks in advance) is necessarily…
The Impact of Data Distribution on Q-learning with Function Approximation
Pedro P. Santos, Diogo S. Carvalho, Alberto Sardinha +1
We study the interplay between the data distribution and Q-learning-based algorithms with function approximation. We provide a unified theoretical and empirical analysis as to how…
CHARET: Character-centered Approach to Emotion Tracking in Stories
Diogo S. Carvalho, Joana Campos, Manuel Guimarães +3
Autonomous agents that can engage in social interactions witha human is the ultimate goal of a myriad of applications. A keychallenge in the design of these applications is to defi…