Agents
A multi-agent reinforcement learning model of common-pool resource appropriation
Humanity faces numerous problems of common-pool resource appropriation. This class of multi-agent social dilemma includes the problems of ensuring sustainable use of fresh water, common fisheries, grazing pastures, and irrigation systems. Abstract models of common-pool resource appropriation based on non-cooperative game theory predict that self-interested agents will generally fail to find socially positive equilibria---a phenomenon called the tragedy of the commons. However, in reality, human societies are sometimes able to discover and implement stable cooperative solutions. Decades of behavioral game theory research have sought to uncover aspects of human behavior that make this possible.
Multiplicative Weights Update with Constant Step-Size in Congestion Games: Convergence, Limit Cycles and Chaos
Gerasimos Palaiopanos, Ioannis Panageas, Georgios Piliouras
Neural Information Processing Systems http://nips.cc/
Robot Talk Episode 134 – Robotics as a hobby, with Kevin McAleer
Claire chatted to Kevin McAleer from kevsrobots about how to get started building robots at home. Kevin McAleer is a hobbyist robotics fanatic who likes to build robots, share videos about them on YouTube and teach people how to do the same. Kev has been building robots since 2019, when he got his first 3d printer and wanted to make more interesting builds. Kev has a degree in Computer Science, and because his day job is relatively hands-off, this hobby allows his creativity to have an outlet. Kev is a huge fan of Python and Micropython for embedded devices, and has a website - kevsrobots.com
Minimizing a Submodular Function from Samples
Submodular functions can be efficiently minimized and are consequently heavily applied in machine learning. There are many cases, however, in which we do not know the function we aim to optimize, but rather have access to training data that is used to learn it.
Tomography of the London Underground: a Scalable Model for Origin-Destination Data
Nicolò Colombo, Ricardo Silva, Soong Moon Kang
Based on the reconstruction of the users' travel time distribution, the model is
Fully Decentralized Policies for Multi-Agent Systems: An Information Theoretic Approach
Roel Dobbe, David Fridovich-Keil, Claire Tomlin
Learning cooperative policies for multi-agent systems is often challenged by partial observability and a lack of coordination.
Dynamic Safe Interruptibility for Decentralized Multi-Agent Reinforcement Learning
El Mahdi El Mhamdi, Rachid Guerraoui, Hadrien Hendrikx, Alexandre Maurer
Neural Information Processing Systems http://nips.cc/
Safe and Nested Subgame Solving for Imperfect-Information Games
In imperfect-information games, the optimal strategy in a subgame may depend on the strategy in other, unreached subgames.
VAIN: Attentional Multi-agent Predictive Modeling
One of the drawbacks of INs is scaling with the number of interactions in the system (typically quadratic or higher order in the number of agents). In this paper we introduce V AIN, a novel attentional architecture for multi-agent predictive modeling that scales linearly with the number of agents. We show that V AIN is effective for multi-agent predictive modeling.
Emergence of Language with Multi-agent Games: Learning to Communicate with Sequences of Symbols
Learning to communicate through interaction, rather than relying on explicit supervision, is often considered a prerequisite for developing a general AI.