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cs.LG2026
A Survey of Multi-Agent Deep Reinforcement Learning with Graph Neural Network-Based Communication
Valentin Cuzin-Rambaud, Laetitia Matignon, Maxime Morge
In multi-agent reinforcement learning (MARL), the integration of a communication mechanism, allowing agents to better learn to coordinate their actions and converge on their object…
cs.LG2021
DisTop: Discovering a Topological representation to learn diverse and rewarding skills
Arthur Aubret, Laetitia matignon, Salima Hassas
The optimal way for a deep reinforcement learning (DRL) agent to explore is to learn a set of skills that achieves a uniform distribution of states. Following this,we introduce Dis…
cs.LG2019
A survey on intrinsic motivation in reinforcement learning
Arthur Aubret, Laetitia Matignon, Salima Hassas
The reinforcement learning (RL) research area is very active, with an important number of new contributions; especially considering the emergent field of deep RL (DRL). However a n…