27 citations · 206 across the 63 of their papers we have counts for
32 papers · 1 filter
TreeHCA: Hindsight-Guided Exploration and Credit Assignment for Multi-Turn LLM Agents
Debajoy Mukherjee, Jeremy Carleton, Sushil Vemuri +2
Outcome-supervised group-relative policy optimization (GRPO) assigns the same advantage to every token in an LLM agent trajectory, leaving useful and unhelpful decisions undifferen…
ReGuide: From Test-Time Guidance to Self-Improving Diffusion Policies
Tzu-Hsiang Lin, Srinivas Shakkottai, Dileep Kalathil +1
Behavior-cloned diffusion policies are expressive but remain vulnerable to covariate shift: small deviations from demonstrated states can compound into task failure. Existing metho…
Reinforcement Learning for Diffusion LLMs with Entropy-Guided Step Selection and Stepwise Advantages
Vishnu Teja Kunde, Fatemeh Doudi, Mahdi Farahbakhsh +3
Reinforcement learning (RL) has been effective for post-training autoregressive (AR) language models, but extending these methods to diffusion language models (DLMs) is challenging…
Optimistic World Models: Efficient Exploration in Model-Based Deep Reinforcement Learning
Akshay Mete, Shahid Aamir Sheikh, Tzu-Hsiang Lin +2
Efficient exploration remains a central challenge in reinforcement learning (RL), particularly in sparse-reward environments. We introduce Optimistic World Models (OWMs), a princip…
GSpaRC: Gaussian Splatting for Real-time Reconstruction of RF Channels
Bhavya Sai Nukapotula, Rishabh Tripathi, Seth Pregler +3
Channel state information (CSI) is essential for adaptive beamforming and maintaining robust links in wireless communication systems. However, acquiring CSI incurs significant over…
MAVIS: Multi-Objective Alignment via Inference-Time Value-Guided Selection
Jeremy Carleton, Debajoy Mukherjee, Srinivas Shakkottai +1
Large Language Models (LLMs) are increasingly deployed across diverse applications that demand balancing multiple, often conflicting, objectives -- such as helpfulness, harmlessnes…
Curriculum Reinforcement Learning from Easy to Hard Tasks Improves LLM Reasoning
Shubham Parashar, Shurui Gui, Xiner Li +8
We aim to improve the reasoning capabilities of language models via reinforcement learning (RL). Recent RL post-trained models like DeepSeek-R1 have demonstrated reasoning abilitie…
Robust LLM Alignment via Distributionally Robust Direct Preference Optimization
Zaiyan Xu, Sushil Vemuri, Kishan Panaganti +3
A major challenge in aligning large language models (LLMs) with human preferences is the issue of distribution shift. LLM alignment algorithms rely on static preference datasets, a…
PowerMamba: A Deep State Space Model and Comprehensive Benchmark for Time Series Prediction in Electric Power Systems
Ali Menati, Fatemeh Doudi, Dileep Kalathil +1
The electricity sector is undergoing substantial transformations due to the rising electrification of demand, enhanced integration of renewable energy resources, and the emergence…
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning
Vasudev Gohil, Satwik Patnaik, Dileep Kalathil +1
Machine learning has shown great promise in addressing several critical hardware security problems. In particular, researchers have developed novel graph neural network (GNN)-based…
Bridging Distributionally Robust Learning and Offline RL: An Approach to Mitigate Distribution Shift and Partial Data Coverage
Kishan Panaganti, Zaiyan Xu, Dileep Kalathil +1
The goal of an offline reinforcement learning (RL) algorithm is to learn optimal polices using historical (offline) data, without access to the environment for online exploration.…
Natural Actor-Critic for Robust Reinforcement Learning with Function Approximation
Ruida Zhou, Tao Liu, Min Cheng +3
We study robust reinforcement learning (RL) with the goal of determining a well-performing policy that is robust against model mismatch between the training simulator and the testi…
Federated Ensemble-Directed Offline Reinforcement Learning
Desik Rengarajan, Nitin Ragothaman, Dileep Kalathil +1
We consider the problem of federated offline reinforcement learning (RL), a scenario under which distributed learning agents must collaboratively learn a high-quality control polic…
Improved Sample Complexity Bounds for Distributionally Robust Reinforcement Learning
Zaiyan Xu, Kishan Panaganti, Dileep Kalathil
We consider the problem of learning a control policy that is robust against the parameter mismatches between the training environment and testing environment. We formulate this as…
Enhanced Meta Reinforcement Learning using Demonstrations in Sparse Reward Environments
Desik Rengarajan, Sapana Chaudhary, Jaewon Kim +2
Meta reinforcement learning (Meta-RL) is an approach wherein the experience gained from solving a variety of tasks is distilled into a meta-policy. The meta-policy, when adapted ov…
DETERRENT: Detecting Trojans using Reinforcement Learning
Vasudev Gohil, Satwik Patnaik, Hao Guo +3
Insertion of hardware Trojans (HTs) in integrated circuits is a pernicious threat. Since HTs are activated under rare trigger conditions, detecting them using random logic simulati…
Meta-Learning Online Control for Linear Dynamical Systems
Deepan Muthirayan, Dileep Kalathil, Pramod P. Khargonekar
In this paper, we consider the problem of finding a meta-learning online control algorithm that can learn across the tasks when faced with a sequence of (similar) control tasks…
Robust Reinforcement Learning using Offline Data
Kishan Panaganti, Zaiyan Xu, Dileep Kalathil +1
The goal of robust reinforcement learning (RL) is to learn a policy that is robust against the uncertainty in model parameters. Parameter uncertainty commonly occurs in many real-w…
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
Ruida Zhou, Tao Liu, Dileep Kalathil +2
We study policy optimization for Markov decision processes (MDPs) with multiple reward value functions, which are to be jointly optimized according to given criteria such as propor…
Reinforcement Learning with Sparse Rewards using Guidance from Offline Demonstration
Desik Rengarajan, Gargi Vaidya, Akshay Sarvesh +2
A major challenge in real-world reinforcement learning (RL) is the sparsity of reward feedback. Often, what is available is an intuitive but sparse reward function that only indica…
Sample Complexity of Robust Reinforcement Learning with a Generative Model
Kishan Panaganti, Dileep Kalathil
The Robust Markov Decision Process (RMDP) framework focuses on designing control policies that are robust against the parameter uncertainties due to the mismatches between the simu…
DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning
Archana Bura, Aria HasanzadeZonuzy, Dileep Kalathil +2
Safe reinforcement learning is extremely challenging--not only must the agent explore an unknown environment, it must do so while ensuring no safety constraint violations. We formu…
Safe Online Convex Optimization with Unknown Linear Safety Constraints
Sapana Chaudhary, Dileep Kalathil
We study the problem of safe online convex optimization, where the action at each time step must satisfy a set of linear safety constraints. The goal is to select a sequence of act…
Online Learning for Predictive Control with Provable Regret Guarantees
Deepan Muthirayan, Jianjun Yuan, Dileep Kalathil +1
We study the problem of online learning in predictive control of an unknown linear dynamical system with time varying cost functions which are unknown apriori. Specifically, we stu…