Author

Ankur Naskar

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Preprint Oct 2026

Sample-Efficient Reinforcement Learning with General Utilities via Neural Dual Critics

Reinforcement learning with general utilities optimizes nonlinear functionals of policy-induced occupancy measures. We develop a natural actor-critic method for concave RLGU objectives using an exact variational dual representation, whose existence follows from concavity and differentiability, with explicit constructio...

Ankur Naskar, Vaneet Aggarwal · 0 citations

Jul 2026

Hierarchical Multilevel Monte Carlo for Order-Optimal Neural Actor-Critic in Average-Reward CMDPs

A hierarchical Multilevel Monte Carlo neural critic that performs debiasing simultaneously across trajectory sampling and critic optimization and establishes the first order-optimal convergence guarantees for infinite-horizon average-reward CMDPs with general policy parameterization and neural critics, while eliminatin...

Ankur Naskar, Vaneet Aggarwal · arXiv.org · 0 citations

Preprint Aug 2026

Finite-Time Analysis of Discounted Exponential-Utility Reinforcement Learning

This work establishes finite-time rates of $\tilde{O} (1/\sqrt{n})$ for the aforementioned two algorithms under asynchronous Markovian sampling, where $n$ is the iteration index and $\tilde{O}$ hides logarithmic expressions.

Ankur Naskar, A. VivekT, Aditya Kumar et al. · 0 citations