Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Ernst, Damien"'
We introduce IMP-MARL, an open-source suite of multi-agent reinforcement learning (MARL) environments for large-scale Infrastructure Management Planning (IMP), offering a platform for benchmarking the scalability of cooperative MARL methods in real-w
Externí odkaz:
http://arxiv.org/abs/2306.11551
In this paper, we identify the best learning scenario to train a team of agents to compete against multiple possible strategies of opposing teams. We evaluate cooperative value-based methods in a mixed cooperative-competitive environment. We restrict
Externí odkaz:
http://arxiv.org/abs/2211.11886
Autor:
Mocanu, Decebal Constantin, Mocanu, Elena, Pinto, Tiago, Curci, Selima, Nguyen, Phuong H., Gibescu, Madeleine, Ernst, Damien, Vale, Zita A.
Publikováno v:
20th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021)
A fundamental task for artificial intelligence is learning. Deep Neural Networks have proven to cope perfectly with all learning paradigms, i.e. supervised, unsupervised, and reinforcement learning. Nevertheless, traditional deep learning approaches
Externí odkaz:
http://arxiv.org/abs/2103.01636
Autor:
Leroy, Pascal, Ernst, Damien, Geurts, Pierre, Louppe, Gilles, Pisane, Jonathan, Sabatelli, Matthia
This paper introduces four new algorithms that can be used for tackling multi-agent reinforcement learning (MARL) problems occurring in cooperative settings. All algorithms are based on the Deep Quality-Value (DQV) family of algorithms, a set of tech
Externí odkaz:
http://arxiv.org/abs/2012.12062