The ability to cooperate through language is a defining feature of humans. As the perceptual, motory and planning capabilities of deep artificial networks increase …
The advances in reinforcement learning have recorded sublime success in various domains. Although the multi-agent domain has been overshadowed by its single-agent counterpart …
Language understanding research is held back by a failure to relate language to the physical world it describes and to the social interactions it facilitates. Despite the incredible …
Problems of cooperation--in which agents seek ways to jointly improve their welfare--are ubiquitous and important. They can be found at scales ranging from our daily routines--such …
In this work we study the problem of using referential language to identify common objects in real-world 3D scenes. We focus on a challenging setup where the referred object belongs to …
We propose a unified mechanism for achieving coordination and communication in Multi- Agent Reinforcement Learning (MARL), through rewarding agents for having causal …
A Warstadt, SR Bowman - Algebraic structures in natural …, 2022 - taylorfrancis.com
Rapid progress in machine learning for natural language processing has the potential to transform debates about how humans learn language. However, the learning environments …
Recent progress in deep learning is essentially based on a “big data for small tasks” paradigm, under which massive amounts of data are used to train a classifier for a single …
Emergent communication aims for a better understanding of human language evolution and building more efficient representations. We posit that reaching these goals will require …