Agents
Not so griddy: Internal representations of RNNs path integrating more than one agent William T. Redman
We find significant differences between these RNNs and those trained to path integrate only a single agent.
Learning the Expected Core of Strictly Convex Stochastic Cooperative Games
Reward allocation, also known as the credit assignment problem, has been an important topic in economics, engineering, and machine learning.
RCDN: Towards Robust Camera-Insensitivity Collaborative Perception via Dynamic Feature-based 3D Neural Modeling
Collaborative perception is dedicated to tackling the constraints of single-agent perception, such as occlusions, based on the multiple agents' multi-view sensor inputs. However, most existing works assume an ideal condition that all agents'
Kaleidoscope: Learnable Masks for Heterogeneous Multi-agent Reinforcement Learning
We further extend Kaleidoscope to critic ensembles in the context of actor-critic algorithms, which could help improve value estimations.
Proportional Fairness in Non-Centroid Clustering
In contrast, we design a new (inefficient) algorithm, GreedyCohesiveClustering, which achieves the relaxation FJR exactly under arbitrary loss functions, and show that the efficient GreedyCapture algorithm achieves a constant approximation of FJR.
