4 citations · 7 across the 6 of their papers we have counts for
5 papers · 1 filter
Robust and Efficient Communication for Multi-Agent Learning
Rafael Pina, Varuna De Silva, Corentin Artaud
Effective communication is a cornerstone of distributed intelligence in Multi-Agent Reinforcement Learning (MARL), yet ensuring that generated messages are both informative and rob…
Fully Independent Communication in Multi-Agent Reinforcement Learning
Rafael Pina, Varuna De Silva, Corentin Artaud +1
Multi-Agent Reinforcement Learning (MARL) comprises a broad area of research within the field of multi-agent systems. Several recent works have focused specifically on the study of…
Staged Reinforcement Learning for Complex Tasks through Decomposed Environments
Rafael Pina, Corentin Artaud, Xiaolan Liu +1
Reinforcement Learning (RL) is an area of growing interest in the field of artificial intelligence due to its many notable applications in diverse fields. Particularly within the c…
Learning Independently from Causality in Multi-Agent Environments
Rafael Pina, Varuna De Silva, Corentin Artaud
Multi-Agent Reinforcement Learning (MARL) comprises an area of growing interest in the field of machine learning. Despite notable advances, there are still problems that require in…
Embedding Contextual Information through Reward Shaping in Multi-Agent Learning: A Case Study from Google Football
Chaoyi Gu, Varuna De Silva, Corentin Artaud +1
Artificial Intelligence has been used to help human complete difficult tasks in complicated environments by providing optimized strategies for decision-making or replacing the manu…