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Found 6 papers, 3 papers with code
On the Role of Information Structure in Reinforcement Learning for Partially-Observable Sequential Teams and Games
In this paper, we argue for the perspective that explicit representation of information structures is an important component of analyzing and solving reinforcement learning problems.
Approximation of relation functions and attention mechanisms
Inner products of neural network feature maps arises in a wide variety of machine learning frameworks as a method of modeling relations between inputs.
Learning Hierarchical Relational Representations through Relational Convolutions
A maturing area of research in deep learning is the study of architectures and inductive biases for learning representations of relational features.
The Relational Bottleneck as an Inductive Bias for Efficient Abstraction
A central challenge for cognitive science is to explain how abstract concepts are acquired from limited experience.
Abstractors and relational cross-attention: An inductive bias for explicit relational reasoning in Transformers
An extension of Transformers is proposed that enables explicit relational reasoning through a novel module called the Abstractor.
Decentralized Multi-Agent Reinforcement Learning for Continuous-Space Stochastic Games
Stochastic games are a popular framework for studying multi-agent reinforcement learning (MARL).
