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cs.LG2026
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…
cs.LG2026
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.…
cs.LG2023
A Theoretical and Empirical Study on the Convergence of Adam with an "Exact" Constant Step Size in Non-Convex Settings
Alokendu Mazumder, Rishabh Sabharwal, Manan Tayal +2
In neural network training, RMSProp and Adam remain widely favoured optimisation algorithms. One of the keys to their performance lies in selecting the correct step size, which can…