5 citations · 21 across the 12 of their papers we have counts for
14 papers
AI Risk Management Should Incorporate Both Safety and Security
Xiangyu Qi, Yangsibo Huang, Yi Zeng +22
The exposure of security vulnerabilities in safety-aligned language models, e.g., susceptibility to adversarial attacks, has shed light on the intricate interplay between AI safety…
A Heterogeneous Agent Model of Mortgage Servicing: An Income-based Relief Analysis
Deepeka Garg, Benjamin Patrick Evans, Leo Ardon +5
Mortgages account for the largest portion of household debt in the United States, totaling around $12 trillion nationwide. In times of financial hardship, alleviating mortgage bur…
O3D: Offline Data-driven Discovery and Distillation for Sequential Decision-Making with Large Language Models
Yuchen Xiao, Yanchao Sun, Mengda Xu +4
Recent advancements in large language models (LLMs) have exhibited promising performance in solving sequential decision-making problems. By imitating few-shot examples provided in…
Heterogeneous Social Value Orientation Leads to Meaningful Diversity in Sequential Social Dilemmas
Udari Madhushani, Kevin R. McKee, John P. Agapiou +6
In social psychology, Social Value Orientation (SVO) describes an individual's propensity to allocate resources between themself and others. In reinforcement learning, SVO has been…
Melting Pot 2.0
John P. Agapiou, Alexander Sasha Vezhnevets, Edgar A. Duéñez-Guzmán +14
Multi-agent artificial intelligence research promises a path to develop intelligent technologies that are more human-like and more human-compatible than those produced by "solipsis…
A Regret Minimization Approach to Multi-Agent Control
Udaya Ghai, Udari Madhushani, Naomi Leonard +1
We study the problem of multi-agent control of a dynamical system with known dynamics and adversarial disturbances. Our study focuses on optimal control without centralized precomp…
One More Step Towards Reality: Cooperative Bandits with Imperfect Communication
Udari Madhushani, Abhimanyu Dubey, Naomi Ehrich Leonard +1
The cooperative bandit problem is increasingly becoming relevant due to its applications in large-scale decision-making. However, most research for this problem focuses exclusively…
When to Call Your Neighbor? Strategic Communication in Cooperative Stochastic Bandits
Udari Madhushani, Naomi Leonard
In cooperative bandits, a framework that captures essential features of collective sequential decision making, agents can minimize group regret, and thereby improve performance, by…
Provably Efficient Multi-Agent Reinforcement Learning with Fully Decentralized Communication
Justin Lidard, Udari Madhushani, Naomi Ehrich Leonard
A challenge in reinforcement learning (RL) is minimizing the cost of sampling associated with exploration. Distributed exploration reduces sampling complexity in multi-agent RL (MA…
Distributed Bandits: Probabilistic Communication on -regular Graphs
Udari Madhushani, Naomi Ehrich Leonard
We study the decentralized multi-agent multi-armed bandit problem for agents that communicate with probability over a network defined by a -regular graph. Every edge in the grap…
On Using Hamiltonian Monte Carlo Sampling for Reinforcement Learning Problems in High-dimension
Udari Madhushani, Biswadip Dey, Naomi Ehrich Leonard +1
Value function based reinforcement learning (RL) algorithms, for example, -learning, learn optimal policies from datasets of actions, rewards, and state transitions. However, wh…
Heterogeneous Explore-Exploit Strategies on Multi-Star Networks
Udari Madhushani, Naomi Leonard
We investigate the benefits of heterogeneity in multi-agent explore-exploit decision making where the goal of the agents is to maximize cumulative group reward. To do so we study a…
Distributed Learning: Sequential Decision Making in Resource-Constrained Environments
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. For distributed l…
A Dynamic Observation Strategy for Multi-agent Multi-armed Bandit Problem
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 cos…