7 citations · 10 across the 10 of their papers we have counts for
10 papers
Cross-fitted Proximal Learning for Model-Based Reinforcement Learning
Nishanth Venkatesh, Andreas A. Malikopoulos
Model-based reinforcement learning is attractive for sequential decision-making because it explicitly estimates reward and transition models and then supports planning through simu…
Model-Based Reinforcement Learning Under Confounding
Nishanth Venkatesh, Andreas A. Malikopoulos
We investigate model-based reinforcement learning in contextual Markov decision processes (C-MDPs) in which the context is unobserved and induces confounding in the offline dataset…
Stochastic Control with Distributionally Robust Constraints for Cyber-Physical Systems Vulnerable to Attacks
Nishanth Venkatesh, Aditya Dave, Ioannis Faros +1
In this paper, we investigate the control of a cyber-physical system (CPS) while accounting for its vulnerability to external attacks. We formulate a constrained stochastic problem…
Connected and Automated Vehicles in Mixed-Traffic: Learning Human Driver Behavior for Effective On-Ramp Merging
Nishanth Venkatesh, Viet-Anh Le, Aditya Dave +1
Highway merging scenarios featuring mixed traffic conditions pose significant modeling and control challenges for connected and automated vehicles (CAVs) interacting with incoming…
Worst-Case Control and Learning Using Partial Observations Over an Infinite Time-Horizon
Aditya Dave, Ioannis Faros, Nishanth Venkatesh +1
Safety-critical cyber-physical systems require control strategies whose worst-case performance is robust against adversarial disturbances and modeling uncertainties. In this paper,…
Approximate Information States for Worst-Case Control and Learning in Uncertain Systems
Aditya Dave, Nishanth Venkatesh, Andreas A. Malikopoulos
In this paper, we investigate discrete-time decision-making problems in uncertain systems with partially observed states. We consider a non-stochastic model, where uncontrolled dis…
On Robust Control of Partially Observed Uncertain Systems with Additive Costs
Aditya Dave, Nishanth Venkatesh, Andreas A. Malikopoulos
In this paper, we consider the problem of optimizing the worst-case behavior of a partially observed system. All uncontrolled disturbances are modeled as finite-valued uncertain va…
Approximate Information States for Worst-case Control of Uncertain Systems
Aditya Dave, Nishanth Venkatesh, Andreas A. Malikopoulos
In this paper, we investigate a worst-case-scenario control problem with a partially observed state. We consider a non-stochastic formulation, where noises and disturbances in our…
Decentralized Control of Two Agents with Nested Accessible Information
Aditya Dave, Nishanth Venkatesh, Andreas A. Malikopoulos
In this paper, we investigate a decentralized stochastic control problem with two agents, where a part of the memory of the second agent is also available to the first agent at eac…
On Decentralized Minimax Control with Nested Subsystems
Aditya Dave, Nishanth Venkatesh, Andreas A. Malikopoulos
In this paper, we investigate a decentralized control problem with nested subsystems, which is a general model for one-directional communication amongst many subsystems. The noises…