research
∙
08/03/2023
Aligning Agent Policy with Externalities: Reward Design via Bilevel RL
In reinforcement learning (RL), a reward function is often assumed at th...
share
research
∙
06/18/2023
On the Global Convergence of Natural Actor-Critic with Two-layer Neural Network Parametrization
Actor-critic algorithms have shown remarkable success in solving state-o...
research
∙
06/09/2023
iPLAN: Intent-Aware Planning in Heterogeneous Traffic via Distributed Multi-Agent Reinforcement Learning
Navigating safely and efficiently in dense and heterogeneous traffic sce...
research
∙
06/09/2023
Ada-NAV: Adaptive Trajectory-Based Sample Efficient Policy Learning for Robotic Navigation
Reinforcement learning methods, while effective for learning robotic nav...
research
∙
04/10/2023
On the Possibilities of AI-Generated Text Detection
Our work focuses on the challenge of detecting outputs generated by Larg...
research
∙
03/14/2023
RE-MOVE: An Adaptive Policy Design Approach for Dynamic Environments via Language-Based Feedback
Reinforcement learning-based policies for continuous control robotic nav...
research
∙
01/28/2023
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic
Many existing reinforcement learning (RL) methods employ stochastic grad...
research
∙
01/28/2023
STEERING: Stein Information Directed Exploration for Model-Based Reinforcement Learning
Directed Exploration is a crucial challenge in reinforcement learning (R...
research
∙
10/25/2022
SWIFT: Rapid Decentralized Federated Learning via Wait-Free Model Communication
The decentralized Federated Learning (FL) setting avoids the role of a p...
research
∙
09/14/2022
Multi Robot Collision Avoidance by Learning Whom to Communicate
Agents in decentralized multi-agent navigation lack the world knowledge ...
research
∙
09/13/2022
RTAW: An Attention Inspired Reinforcement Learning Method for Multi-Robot Task Allocation in Warehouse Environments
We present a novel reinforcement learning based algorithm for multi-robo...
research
∙
09/07/2022
DC-MRTA: Decentralized Multi-Robot Task Allocation and Navigation in Complex Environments
We present a novel reinforcement learning (RL) based task allocation and...
research
∙
07/08/2022
HTRON:Efficient Outdoor Navigation with Sparse Rewards via Heavy Tailed Adaptive Reinforce Algorithm
We present a novel approach to improve the performance of deep reinforce...
research
∙
06/22/2022
: Calibrating Global and Local Models via Federated Learning Beyond Consensus
In federated learning (FL), the objective of collaboratively learning a ...
research
∙
06/12/2022
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Conservative Natural Policy Gradient Primal-Dual Algorithm
We consider the problem of constrained Markov decision process (CMDP) in...
research
∙
06/12/2022
Dealing with Sparse Rewards in Continuous Control Robotics via Heavy-Tailed Policies
In this paper, we present a novel Heavy-Tailed Stochastic Policy Gradien...
research
∙
06/02/2022
Posterior Coreset Construction with Kernelized Stein Discrepancy for Model-Based Reinforcement Learning
In this work, we propose a novel Kernelized Stein Discrepancy-based Post...
research
∙
03/02/2022
Distributed Riemannian Optimization with Lazy Communication for Collaborative Geometric Estimation
We present the first distributed optimization algorithm with lazy commun...
research
∙
01/28/2022
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
We focus on parameterized policy search for reinforcement learning over ...
research
∙
10/22/2021
Projection-Free Algorithm for Stochastic Bi-level Optimization
This work presents the first projection-free algorithm to solve stochast...
research
∙
10/22/2021
Convergence Rates of Average-Reward Multi-agent Reinforcement Learning via Randomized Linear Programming
In tabular multi-agent reinforcement learning with average-cost criterio...
research
∙
09/13/2021
Achieving Zero Constraint Violation for Constrained Reinforcement Learning via Primal-Dual Approach
Reinforcement learning is widely used in applications where one needs to...
research
∙
07/26/2021
Wasserstein-Splitting Gaussian Process Regression for Heterogeneous Online Bayesian Inference
Gaussian processes (GPs) are a well-known nonparametric Bayesian inferen...
research
∙
06/15/2021
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control
Reinforcement learning is a framework for interactive decision-making wi...
research
∙
05/29/2021
MARL with General Utilities via Decentralized Shadow Reward Actor-Critic
We posit a new mechanism for cooperation in multi-agent reinforcement le...
research
∙
08/13/2020
Conservative Stochastic Optimization with Expectation Constraints
This paper considers stochastic convex optimization problems where the o...
research
∙
07/04/2020
Variational Policy Gradient Method for Reinforcement Learning with General Utilities
In recent years, reinforcement learning (RL) systems with general goals ...
research
∙
03/23/2020
Efficient Gaussian Process Bandits by Believing only Informative Actions
Bayesian optimization is a framework for global search via maximum a pos...
research
∙
03/06/2020
Asynchronous and Parallel Distributed Pose Graph Optimization
We present Asynchronous Stochastic Parallel Pose Graph Optimization (ASA...
research
∙
02/27/2020
Cautious Reinforcement Learning via Distributional Risk in the Dual Domain
We study the estimation of risk-sensitive policies in reinforcement lear...
research
∙
09/25/2019
Optimally Compressed Nonparametric Online Learning
Batch training of machine learning models based on neural networks is no...
research
∙
09/23/2019
Approximate Shannon Sampling in Importance Sampling: Nearly Consistent Finite Particle Estimates
In Bayesian inference, we seek to compute information about random varia...
research
∙
09/12/2019
Nonstationary Nonparametric Online Learning: Balancing Dynamic Regret and Model Parsimony
An open challenge in supervised learning is conceptual drift: a data poi...
research
∙
08/01/2019
Adaptive Kernel Learning in Heterogeneous Networks
We consider the framework of learning over decentralized networks, where...
research
∙
05/16/2019