4 papers
Bayesian Learning in Episodic Zero-Sum Games
Chang-Wei Yueh, Andy Zhao, Ashutosh Nayyar +1
We study Bayesian learning in episodic, finite-horizon zero-sum Markov games with unknown transition and reward models. We investigate a posterior algorithm in which each player ma…
Balance Equation-based Distributionally Robust Offline Imitation Learning
Rishabh Agrawal, Yusuf Alvi, Rahul Jain +1
Imitation Learning (IL) has proven highly effective for robotic and control tasks where manually designing reward functions or explicit controllers is infeasible. However, standard…
Compositional Planning for Logically Constrained Multi-Agent Markov Decision Processes
Krishna C. Kalagarla, Matthew Low, Rahul Jain +2
Designing control policies for large, distributed systems is challenging, especially in the context of critical, temporal logic based specifications (e.g., safety) that must be met…
Markov Balance Satisfaction Improves Performance in Strictly Batch Offline Imitation Learning
Rishabh Agrawal, Nathan Dahlin, Rahul Jain +1
Imitation learning (IL) is notably effective for robotic tasks where directly programming behaviors or defining optimal control costs is challenging. In this work, we address a sce…