4 citations · 6 across the 9 of their papers we have counts for
4 papers · 1 filter
Capacity-Aware Planning and Scheduling in Budget-Constrained Multi-Agent MDPs: A Meta-RL Approach
Manav Vora, Ilan Shomorony, Melkior Ornik
We study capacity- and budget-constrained multi-agent MDPs (CB-MA-MDPs), a class that captures many maintenance and scheduling tasks in which each agent can irreversibly fail and a…
Solving Truly Massive Budgeted Monotonic POMDPs with Oracle-Guided Meta-Reinforcement Learning
Manav Vora, Jonas Liang, Michael N. Grussing +1
Monotonic Partially Observable Markov Decision Processes (POMDPs), where the system state progressively decreases until a restorative action is performed, can be used to model sequ…
A Moral Imperative: The Need for Continual Superalignment of Large Language Models
Gokul Puthumanaillam, Manav Vora, Pranay Thangeda +1
This paper examines the challenges associated with achieving life-long superalignment in AI systems, particularly large language models (LLMs). Superalignment is a theoretical fram…
ComTraQ-MPC: Meta-Trained DQN-MPC Integration for Trajectory Tracking with Limited Active Localization Updates
Gokul Puthumanaillam, Manav Vora, Melkior Ornik
Optimal decision-making for trajectory tracking in partially observable, stochastic environments where the number of active localization updates -- the process by which the agent o…