no code implementations • 8 Mar 2024 • Ian Xul Belaustegui, Marcela Ordorica Arango, Román Rossi-Pool, Naomi Ehrich Leonard, Alessio Franci

An important problem in many areas of science is that of recovering interaction networks from simultaneous time-series of many interacting dynamical processes.

no code implementations • 3 Nov 2023 • Anastasia Bizyaeva, Marcela Ordorica Arango, Yunxiu Zhou, Simon Levin, Naomi Ehrich Leonard

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.

2 code implementations • 24 Aug 2023 • Justice Mason, Christine Allen-Blanchette, Nicholas Zolman, Elizabeth Davison, Naomi Ehrich Leonard

In many real-world settings, image observations of freely rotating 3D rigid bodies may be available when low-dimensional measurements are not.

no code implementations • 13 Jun 2023 • Shinkyu Park, Naomi Ehrich Leonard

Their goal is to learn the strategies of the Nash equilibrium of the game.

1 code implementation • 5 Apr 2023 • Haimin Hu, Kensuke Nakamura, Kai-Chieh Hsu, Naomi Ehrich Leonard, Jaime Fernández Fisac

We present a multi-agent decision-making framework for the emergent coordination of autonomous agents whose intents are initially undecided.

no code implementations • NeurIPS 2021 • Udari Madhushani, Abhimanyu Dubey, Naomi Ehrich Leonard, Alex Pentland

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.

no code implementations • 14 Oct 2021 • Justin Lidard, Udari Madhushani, Naomi Ehrich Leonard

Distributed exploration reduces sampling complexity in multi-agent RL (MARL).

no code implementations • 16 Nov 2020 • Udari Madhushani, Naomi Ehrich Leonard

Every edge in the graph has probabilistic weight $p$ to account for the ($1\!-\! p$) probability of a communication link failure.

no code implementations • 11 Nov 2020 • Udari Madhushani, Biswadip Dey, Naomi Ehrich Leonard, Amit Chakraborty

Value function based reinforcement learning (RL) algorithms, for example, $Q$-learning, learn optimal policies from datasets of actions, rewards, and state transitions.

no code implementations • 24 Oct 2020 • Christine Allen-Blanchette, Sushant Veer, Anirudha Majumdar, Naomi Ehrich Leonard

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.

1 code implementation • NeurIPS 2020 • Yaofeng Desmond Zhong, Naomi Ehrich Leonard

The VAE is designed to account for the geometry of physical systems composed of multiple rigid bodies in the plane.

no code implementations • 13 Apr 2020 • Udari Madhushani, Naomi Ehrich Leonard

We study cost-effective communication strategies that can be used to improve the performance of distributed learning systems in resource-constrained environments.

no code implementations • 8 Apr 2020 • Udari Madhushani, Naomi Ehrich Leonard

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.

no code implementations • 3 Mar 2020 • Peter Landgren, Vaibhav Srivastava, Naomi Ehrich Leonard

And we consider a constrained reward model in which agents that choose the same arm at the same time receive no reward.

no code implementations • 21 May 2019 • Udari Madhushani, Naomi Ehrich Leonard

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.

no code implementations • 2 Jun 2016 • Peter Landgren, Vaibhav Srivastava, Naomi Ehrich Leonard

We study distributed cooperative decision-making under the explore-exploit tradeoff in the multiarmed bandit (MAB) problem.

no code implementations • 23 Dec 2015 • Paul Reverdy, Vaibhav Srivastava, Naomi Ehrich Leonard

Satisficing is a relaxation of maximizing and allows for less risky decision making in the face of uncertainty.

no code implementations • 21 Dec 2015 • Peter Landgren, Vaibhav Srivastava, Naomi Ehrich Leonard

We study the explore-exploit tradeoff in distributed cooperative decision-making using the context of the multiarmed bandit (MAB) problem.

no code implementations • 5 Jul 2015 • Vaibhav Srivastava, Paul Reverdy, Naomi Ehrich Leonard

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.

no code implementations • 11 Sep 2012 • Naomi Ehrich Leonard, Alex Olshevsky

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.

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