22 citations · 96 across the 26 of their papers we have counts for
33 papers
Conditional Kernel Imitation Learning for Continuous State Environments
Rishabh Agrawal, Nathan Dahlin, Rahul Jain +1
Imitation Learning (IL) is an important paradigm within the broader reinforcement learning (RL) methodology. Unlike most of RL, it does not assume availability of reward-feedback.…
Bridging Imitation and Online Reinforcement Learning: An Optimistic Tale
Botao Hao, Rahul Jain, Dengwang Tang +1
In this paper, we address the following problem: Given an offline demonstration dataset from an imperfect expert, what is the best way to leverage it to bootstrap online learning p…
Leveraging Demonstrations to Improve Online Learning: Quality Matters
Botao Hao, Rahul Jain, Tor Lattimore +2
We investigate the extent to which offline demonstration data can improve online learning. It is natural to expect some improvement, but the question is how, and by how much? We sh…
Safe Posterior Sampling for Constrained MDPs with Bounded Constraint Violation
Krishna C Kalagarla, Rahul Jain, Pierluigi Nuzzo
Constrained Markov decision processes (CMDPs) model scenarios of sequential decision making with multiple objectives that are increasingly important in many applications. However,…
Optimal Control of Partially Observable Markov Decision Processes with Finite Linear Temporal Logic Constraints
Krishna C. Kalagarla, Dhruva Kartik, Dongming Shen +3
Autonomous agents often operate in scenarios where the state is partially observed. In addition to maximizing their cumulative reward, agents must execute complex tasks with rich t…
Improved No-Regret Algorithms for Stochastic Shortest Path with Linear MDP
Liyu Chen, Rahul Jain, Haipeng Luo
We introduce two new no-regret algorithms for the stochastic shortest path (SSP) problem with a linear MDP that significantly improve over the only existing results of (Vial et al.…