99 citations · 196 across the 39 of their papers we have counts for
6 papers · 1 filter
Posterior Sampling-based Online Learning for Episodic POMDPs
Dengwang Tang, Dongze Ye, Rahul Jain +2
Learning in POMDPs is known to be significantly harder than in MDPs. In this paper, we consider the online learning problem for episodic POMDPs with unknown transition and observat…
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.…
A Bayesian Learning Algorithm for Unknown Zero-sum Stochastic Games with an Arbitrary Opponent
Mehdi Jafarnia-Jahromi, Rahul Jain, Ashutosh Nayyar
In this paper, we propose Posterior Sampling Reinforcement Learning for Zero-sum Stochastic Games (PSRL-ZSG), the first online learning algorithm that achieves Bayesian regret boun…
Online Learning for Unknown Partially Observable MDPs
Mehdi Jafarnia-Jahromi, Rahul Jain, Ashutosh Nayyar
Solving Partially Observable Markov Decision Processes (POMDPs) is hard. Learning optimal controllers for POMDPs when the model is unknown is harder. Online learning of optimal con…
Regret Bounds for Decentralized Learning in Cooperative Multi-Agent Dynamical Systems
Seyed Mohammad Asghari, Yi Ouyang, Ashutosh Nayyar
Regret analysis is challenging in Multi-Agent Reinforcement Learning (MARL) primarily due to the dynamical environments and the decentralized information among agents. We attempt t…
Learning Unknown Markov Decision Processes: A Thompson Sampling Approach
Yi Ouyang, Mukul Gagrani, Ashutosh Nayyar +1
We consider the problem of learning an unknown Markov Decision Process (MDP) that is weakly communicating in the infinite horizon setting. We propose a Thompson Sampling-based rein…