2 citations · 4 across the 18 of their papers we have counts for
7 papers · 1 filter
Kolmogorov Regression for Robust Diffusion Policies
Lekan Molu
Finite-dimensional (FD) diffusion policies exhibit temporal drift owing to discretization artifacts that degrade long-horizon performance (when deployed on physical systems). We in…
AdaFair-MARL: Enforcing Adaptive Fairness Constraints in Multi-Agent Reinforcement Learning
Promise Ekpo, Saesha Agarwal, Felix Grimm +2
Fair workload enforcement in heterogeneous multi-agent systems that pursue shared objectives remains challenging. Fixed fairness penalties often introduce inefficiencies, training…
Is Bellman Equation Enough for Learning Control?
Haoxiang You, Lekan Molu, Ian Abraham
The Bellman equation and its continuous-time counterpart, the Hamilton-Jacobi-Bellman (HJB) equation, serve as necessary conditions for optimality in reinforcement learning and opt…
Verification-Aided Learning of Neural Network Barrier Functions with Termination Guarantees
Shaoru Chen, Lekan Molu, Mahyar Fazlyab
Barrier functions are a general framework for establishing a safety guarantee for a system. However, there is no general method for finding these functions. To address this shortco…
PcLast: Discovering Plannable Continuous Latent States
Anurag Koul, Shivakanth Sujit, Shaoru Chen +11
Goal-conditioned planning benefits from learned low-dimensional representations of rich observations. While compact latent representations typically learned from variational autoen…
Guaranteed Discovery of Control-Endogenous Latent States with Multi-Step Inverse Models
Alex Lamb, Riashat Islam, Yonathan Efroni +7
In many sequential decision-making tasks, the agent is not able to model the full complexity of the world, which consists of multitudes of relevant and irrelevant information. For…
Interaction-Grounded Learning with Action-inclusive Feedback
Tengyang Xie, Akanksha Saran, Dylan J. Foster +5
Consider the problem setting of Interaction-Grounded Learning (IGL), in which a learner's goal is to optimally interact with the environment with no explicit reward to ground its p…