6 papers
Safe Flow Q-Learning: Offline Safe Reinforcement Learning with Reachability-Based Flow Policies
Mumuksh Tayal, Manan Tayal, Ravi Prakash
Offline safe reinforcement learning (RL) seeks reward-maximizing policies from static datasets under strict safety constraints. Existing methods often rely on soft expected-cost ob…
V-OCBF: Learning Safety Filters from Offline Data via Value-Guided Offline Control Barrier Functions
Mumuksh Tayal, Manan Tayal, Aditya Singh +2
Ensuring safety in autonomous systems requires controllers that aim to satisfy state-wise constraints without relying on online interaction.While existing Safe Offline RL methods t…
Epigraph-Guided Flow Matching for Safe and Performant Offline Reinforcement Learning
Manan Tayal, Mumuksh Tayal
Offline reinforcement learning (RL) provides a compelling paradigm for training autonomous systems without the risks of online exploration, particularly in safety-critical domains.…
RISE: Robust Imitation through Stochastic Encoding
Mumuksh Tayal, Manan Tayal, Ravi Prakash
Ensuring safety in robotic systems remains a fundamental challenge, especially when deploying offline policy-learning methods such as imitation learning in dynamic environments. Tr…
A Fixed Parameter Tractable Approach for Solving the Vertex Cover Problem in Polynomial Time Complexity
Mumuksh Tayal
The Minimum Vertex Cover problem, a classical NP-complete problem, presents significant challenges for exact solution on large graphs. Fixed-Parameter Tractability (FPT) offers a p…
Evaluating Multi-Instance DNN Inferencing on Multiple Accelerators of an Edge Device
Mumuksh Tayal, Yogesh Simmhan
Edge devices like Nvidia Jetson platforms now offer several on-board accelerators -- including GPU CUDA cores, Tensor Cores, and Deep Learning Accelerators (DLA) -- which can be co…