4 citations · 7 across the 9 of their papers we have counts for
5 papers · 1 filter
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…
Discovering Causality for Efficient Cooperation in Multi-Agent Environments
Rafael Pina, Varuna De Silva, Corentin Artaud
In cooperative Multi-Agent Reinforcement Learning (MARL) agents are required to learn behaviours as a team to achieve a common goal. However, while learning a task, some agents may…
Causality Detection for Efficient Multi-Agent Reinforcement Learning
Rafael Pina, Varuna De Silva, Corentin Artaud
When learning a task as a team, some agents in Multi-Agent Reinforcement Learning (MARL) may fail to understand their true impact in the performance of the team. Such agents end up…
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…