1 code implementation • 12 Oct 2022 • Pedro P. Santos, Diogo S. Carvalho, Miguel Vasco, Alberto Sardinha, Pedro A. Santos, Ana Paiva, Francisco S. Melo
We introduce hybrid execution in multi-agent reinforcement learning (MARL), a new paradigm in which agents aim to successfully perform cooperative tasks with any communication level at execution time by taking advantage of information-sharing among the agents.
Multi-agent Reinforcement Learning reinforcement-learning +1
no code implementations • 6 Mar 2022 • Diogo S. Carvalho, Biswa Sengupta
In this work, we set ourselves on a problem that presents itself with a hierarchical structure: the task-scheduling, by a centralised agent, in a dynamic warehouse multi-agent environment and the execution of one such schedule, by decentralised agents with only partial observability thereof.
1 code implementation • 23 Nov 2021 • Pedro P. Santos, Diogo S. Carvalho, Alberto Sardinha, Francisco S. Melo
We provide a unified theoretical and empirical analysis as to how different properties of the data distribution influence the performance of Q-learning-based algorithms.
no code implementations • 15 Feb 2021 • Diogo S. Carvalho, Joana Campos, Manuel Guimarães, Ana Antunes, João Dias, Pedro A. Santos
Autonomous agents that can engage in social interactions witha human is the ultimate goal of a myriad of applications.