no code implementations • 23 Sep 2024 • Pedro P. Santos, Alberto Sardinha, Francisco S. Melo
We show that, as opposed to standard MDPs, the number of trials plays a key-role in infinite-horizon GUMDPs and the expected performance of a given policy depends, in general, on the number of trials.
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 complete cooperative tasks with arbitrary communication levels at execution time by taking advantage of information-sharing among the agents.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
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 • 24 Jan 2021 • Guilherme S. Varela, Pedro P. Santos, Alberto Sardinha, Francisco S. Melo
Our methodology addresses the lack of standardization in the literature that renders the comparison of approaches in different works meaningless, due to differences in metrics, environments, and even experimental design and methodology.