Search Results for author:
Found 18 papers, 9 papers with code
IIKL: Isometric Immersion Kernel Learning with Riemannian Manifold for Geometric Preservation
In this paper, we propose a novel Isometric Immersion Kernel Learning (IIKL) method to build Riemannian manifold and isometrically induce Riemannian metric from discrete non-Euclidean data.
Agent S2: A Compositional Generalist-Specialist Framework for Computer Use Agents
Computer use agents automate digital tasks by directly interacting with graphical user interfaces (GUIs) on computers and mobile devices, offering significant potential to enhance human productivity by completing an open-ended space of user queries.
DisCo-DSO: Coupling Discrete and Continuous Optimization for Efficient Generative Design in Hybrid Spaces
We consider the challenge of black-box optimization within hybrid discrete-continuous and variable-length spaces, a problem that arises in various applications, such as decision tree learning and symbolic regression.
Agent S: An Open Agentic Framework that Uses Computers Like a Human
We present Agent S, an open agentic framework that enables autonomous interaction with computers through a Graphical User Interface (GUI), aimed at transforming human-computer interaction by automating complex, multi-step tasks.
Multi-Agent Reinforcement Learning for Adaptive Mesh Refinement
Comprehensive experiments show that VDGN policies significantly outperform error threshold-based policies in global error and cost metrics.
Multi-agent Reinforcement Learning
reinforcement-learning
+2
Do Deep Neural Networks Always Perform Better When Eating More Data?
Data has now become a shortcoming of deep learning.
A Principled Permutation Invariant Approach to Mean-Field Multi-Agent Reinforcement Learning
To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation- invariant actor-critic neural architecture.
Permutation Invariant Policy Optimization for Mean-Field Multi-Agent Reinforcement Learning: A Principled Approach
To exploit the permutation invariance therein, we propose the mean-field proximal policy optimization (MF-PPO) algorithm, at the core of which is a permutation-invariant actor-critic neural architecture.
Reinforcement Learning for Adaptive Mesh Refinement
Large-scale finite element simulations of complex physical systems governed by partial differential equations (PDE) crucially depend on adaptive mesh refinement (AMR) to allocate computational budget to regions where higher resolution is required.
GraphOpt: Learning Optimization Models of Graph Formation
Formation mechanisms are fundamental to the study of complex networks, but learning them from observations is challenging.
Learning to Incentivize Other Learning Agents
The challenge of developing powerful and general Reinforcement Learning (RL) agents has received increasing attention in recent years.
Hierarchical Cooperative Multi-Agent Reinforcement Learning with Skill Discovery
The interpretability of the learned skills show the promise of the proposed method for achieving human-AI cooperation in team sports games.
Single Episode Policy Transfer in Reinforcement Learning
An even greater challenge is performing near-optimally in a single attempt at test time, possibly without access to dense rewards, which is not addressed by current methods that require multiple experience rollouts for adaptation.
Integrating independent and centralized multi-agent reinforcement learning for traffic signal network optimization
Traffic congestion in metropolitan areas is a world-wide problem that can be ameliorated by traffic lights that respond dynamically to real-time conditions.
Deep Reinforcement Learning
Multi-agent Reinforcement Learning
+2
CM3: Cooperative Multi-goal Multi-stage Multi-agent Reinforcement Learning
To address both challenges, we restructure the problem into a novel two-stage curriculum, in which single-agent goal attainment is learned prior to learning multi-agent cooperation, and we derive a new multi-goal multi-agent policy gradient with a credit function for localized credit assignment.
Precision medicine as a control problem: Using simulation and deep reinforcement learning to discover adaptive, personalized multi-cytokine therapy for sepsis
To the best of our knowledge, this work is the first to consider adaptive, personalized multi-cytokine mediation therapy for sepsis, and is the first to exploit deep reinforcement learning on a biological simulation.
Learning Deep Mean Field Games for Modeling Large Population Behavior
We consider the problem of representing collective behavior of large populations and predicting the evolution of a population distribution over a discrete state space.
Fake News Mitigation via Point Process Based Intervention
We propose the first multistage intervention framework that tackles fake news in social networks by combining reinforcement learning with a point process network activity model.
