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Found 13 papers, 3 papers with code
Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas
In social psychology, Social Value Orientation (SVO) describes an individual's propensity to allocate resources between themself and others.
Developing, Evaluating and Scaling Learning Agents in Multi-Agent Environments
The Game Theory & Multi-Agent team at DeepMind studies several aspects of multi-agent learning ranging from computing approximations to fundamental concepts in game theory to simulating social dilemmas in rich spatial environments and training 3-d humanoids in difficult team coordination tasks.
Stochastic Parallelizable Eigengap Dilation for Large Graph Clustering
A core step of spectral clustering is performing an eigendecomposition of the corresponding graph Laplacian matrix (or equivalently, a singular value decomposition, SVD, of the incidence matrix).
Learning Robust Real-Time Cultural Transmission without Human Data
We provide a method for generating zero-shot, high recall cultural transmission in artificially intelligent agents.
Hidden Agenda: a Social Deduction Game with Diverse Learned Equilibria
A key challenge in the study of multiagent cooperation is the need for individual agents not only to cooperate effectively, but to decide with whom to cooperate.
Collaborating with Humans without Human Data
Here, we study the problem of how to train agents that collaborate well with human partners without using human data.
Quantifying the effects of environment and population diversity in multi-agent reinforcement learning
Generalization is a major challenge for multi-agent reinforcement learning.
Modelling Cooperation in Network Games with Spatio-Temporal Complexity
Such systems have local incentives for individuals, whose behavior has an impact on the global outcome for the group.
Negotiating Team Formation Using Deep Reinforcement Learning
When autonomous agents interact in the same environment, they must often cooperate to achieve their goals.
Learning to Play No-Press Diplomacy with Best Response Policy Iteration
It also features a large combinatorial action space and simultaneous moves, which are challenging for RL algorithms.
Uncovering Surprising Behaviors in Reinforcement Learning via Worst-case Analysis
Reinforcement learning agents are typically trained and evaluated according to their performance averaged over some distribution of environment settings.
Identifying Sources and Sinks in the Presence of Multiple Agents with Gaussian Process Vector Calculus
In systems of multiple agents, identifying the cause of observed agent dynamics is challenging.
Inferring agent objectives at different scales of a complex adaptive system
It has been suggested that multiple agent classes operate in this system, with a non-trivial hierarchy of top-down and bottom-up causation classes with different effective models governing each level.
