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Found 20 papers, 3 papers with code
Sparse dynamic network reconstruction through L1-regularization of a Lyapunov equation
An important problem in many areas of science is that of recovering interaction networks from simultaneous time-series of many interacting dynamical processes.
Active risk aversion in SIS epidemics on networks
We prove that the model, with these two networks and populations using risk aversion strategies, exhibits a transcritical bifurcation in which an endemic equilibrium emerges.
Learning to Predict 3D Rotational Dynamics from Images of a Rigid Body with Unknown Mass Distribution
In many real-world settings, image observations of freely rotating 3D rigid bodies may be available when low-dimensional measurements are not.
Learning with Delayed Payoffs in Population Games using Kullback-Leibler Divergence Regularization
Their goal is to learn the strategies of the Nash equilibrium of the game.
Emergent Coordination through Game-Induced Nonlinear Opinion Dynamics
We present a multi-agent decision-making framework for the emergent coordination of autonomous agents whose intents are initially undecided.
One More Step Towards Reality: Cooperative Bandits with Imperfect Communication
However, most research for this problem focuses exclusively on the setting with perfect communication, whereas in most real-world distributed settings, communication is often over stochastic networks, with arbitrary corruptions and delays.
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication
Distributed exploration reduces sampling complexity in multi-agent RL (MARL).
Distributed Bandits: Probabilistic Communication on $d$-regular Graphs
Every edge in the graph has probabilistic weight $p$ to account for the ($1\!-\! p$) probability of a communication link failure.
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension
Value function based reinforcement learning (RL) algorithms, for example, $Q$-learning, learn optimal policies from datasets of actions, rewards, and state transitions.
LagNetViP: A Lagrangian Neural Network for Video Prediction
In this paper, we introduce a video prediction model where the equations of motion are explicitly constructed from learned representations of the underlying physical quantities.
Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control
The VAE is designed to account for the geometry of physical systems composed of multiple rigid bodies in the plane.
Distributed Learning: Sequential Decision Making in Resource-Constrained Environments
We study cost-effective communication strategies that can be used to improve the performance of distributed learning systems in resource-constrained environments.
A Dynamic Observation Strategy for Multi-agent Multi-armed Bandit Problem
We define and analyze a multi-agent multi-armed bandit problem in which decision-making agents can observe the choices and rewards of their neighbors under a linear observation cost.
Distributed Cooperative Decision Making in Multi-agent Multi-armed Bandits
And we consider a constrained reward model in which agents that choose the same arm at the same time receive no reward.
Heterogeneous Stochastic Interactions for Multiple Agents in a Multi-armed Bandit Problem
We define and analyze a multi-agent multi-armed bandit problem in which decision-making agents can observe the choices and rewards of their neighbors.
Distributed Cooperative Decision-Making in Multiarmed Bandits: Frequentist and Bayesian Algorithms
We study distributed cooperative decision-making under the explore-exploit tradeoff in the multiarmed bandit (MAB) problem.
Satisficing in multi-armed bandit problems
Satisficing is a relaxation of maximizing and allows for less risky decision making in the face of uncertainty.
On Distributed Cooperative Decision-Making in Multiarmed Bandits
We study the explore-exploit tradeoff in distributed cooperative decision-making using the context of the multiarmed bandit (MAB) problem.
Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis
We consider the correlated multiarmed bandit (MAB) problem in which the rewards associated with each arm are modeled by a multivariate Gaussian random variable, and we investigate the influence of the assumptions in the Bayesian prior on the performance of the upper credible limit (UCL) algorithm and a new correlated UCL algorithm.
Cooperative learning in multi-agent systems from intermittent measurements
Motivated by the problem of tracking a direction in a decentralized way, we consider the general problem of cooperative learning in multi-agent systems with time-varying connectivity and intermittent measurements.
