19 citations · 24 across the 6 of their papers we have counts for
6 papers
Beyond Exponentially Fast Mixing in Average-Reward Reinforcement Learning via Multi-Level Monte Carlo Actor-Critic
Wesley A. Suttle, Amrit Singh Bedi, Bhrij Patel +3
Many existing reinforcement learning (RL) methods employ stochastic gradient iteration on the back end, whose stability hinges upon a hypothesis that the data-generating process mi…
LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models
Chan Hee Song, Jiaman Wu, Clayton Washington +3
This study focuses on using large language models (LLMs) as a planner for embodied agents that can follow natural language instructions to complete complex tasks in a visually-perc…
Identifying Coordination in a Cognitive Radar Network -- A Multi-Objective Inverse Reinforcement Learning Approach
Luke Snow, Vikram Krishnamurthy, Brian M. Sadler
Consider a target being tracked by a cognitive radar network. If the target can intercept some radar network emissions, how can it detect coordination among the radars? By 'coordin…
FedBC: Calibrating Global and Local Models via Federated Learning Beyond Consensus
Amrit Singh Bedi, Chen Fan, Alec Koppel +4
In this work, we quantitatively calibrate the performance of global and local models in federated learning through a multi-criterion optimization-based framework, which we cast as…
Deceptive Planning for Resource Allocation
Shenghui Chen, Yagiz Savas, Mustafa O. Karabag +2
We consider a team of autonomous agents that navigate in an adversarial environment and aim to achieve a task by allocating their resources over a set of target locations. An adver…
On the Hidden Biases of Policy Mirror Ascent in Continuous Action Spaces
Amrit Singh Bedi, Souradip Chakraborty, Anjaly Parayil +3
We focus on parameterized policy search for reinforcement learning over continuous action spaces. Typically, one assumes the score function associated with a policy is bounded, whi…