11 citations · 47 across the 9 of their papers we have counts for
9 papers
On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL
Jinglin Chen, Aditya Modi, Akshay Krishnamurthy +2
We study reward-free reinforcement learning (RL) under general non-linear function approximation, and establish sample efficiency and hardness results under various standard struct…
Joint Learning-Based Stabilization of Multiple Unknown Linear Systems
Mohamad Kazem Shirani Faradonbeh, Aditya Modi
Learning-based control of linear systems received a lot of attentions recently. In popular settings, the true dynamical models are unknown to the decision-maker and need to be inte…
Joint Learning of Linear Time-Invariant Dynamical Systems
Aditya Modi, Mohamad Kazem Shirani Faradonbeh, Ambuj Tewari +1
Linear time-invariant systems are very popular models in system theory and applications. A fundamental problem in system identification that remains rather unaddressed in extant li…
Model-free Representation Learning and Exploration in Low-rank MDPs
Aditya Modi, Jinglin Chen, Akshay Krishnamurthy +2
The low rank MDP has emerged as an important model for studying representation learning and exploration in reinforcement learning. With a known representation, several model-free e…
Clinician-in-the-Loop Decision Making: Reinforcement Learning with Near-Optimal Set-Valued Policies
Shengpu Tang, Aditya Modi, Michael W. Sjoding +1
Standard reinforcement learning (RL) aims to find an optimal policy that identifies the best action for each state. However, in healthcare settings, many actions may be near-equiva…
Sample Complexity of Reinforcement Learning using Linearly Combined Model Ensembles
Aditya Modi, Nan Jiang, Ambuj Tewari +1
Reinforcement learning (RL) methods have been shown to be capable of learning intelligent behavior in rich domains. However, this has largely been done in simulated domains without…
Metareasoning in Modular Software Systems: On-the-Fly Configuration using Reinforcement Learning with Rich Contextual Representations
Aditya Modi, Debadeepta Dey, Alekh Agarwal +4
Assemblies of modular subsystems are being pressed into service to perform sensing, reasoning, and decision making in high-stakes, time-critical tasks in such areas as transportati…
No-regret Exploration in Contextual Reinforcement Learning
Aditya Modi, Ambuj Tewari
We consider the recently proposed reinforcement learning (RL) framework of Contextual Markov Decision Processes (CMDP), where the agent interacts with a (potentially adversarial) s…
Markov Decision Processes with Continuous Side Information
Aditya Modi, Nan Jiang, Satinder Singh +1
We consider a reinforcement learning (RL) setting in which the agent interacts with a sequence of episodic MDPs. At the start of each episode the agent has access to some side-info…