5 papers
When Dynamics Shift, Robust Task Inference Wins: Offline Imitation Learning with Behavior Foundation Models Revisited
Rishabh Agrawal, Rahul Jain, Ashutosh Nayyar
Behavior Foundation Models (BFMs) enable scalable imitation learning (IL) by pretraining task-agnostic representations that can be rapidly adapted to new tasks. However, existing B…
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…
Posterior Sampling-based Online Learning for Episodic POMDPs
Dengwang Tang, Dongze Ye, Rahul Jain +2
Learning in POMDPs is known to be significantly harder than in MDPs. In this paper, we consider the online learning problem for episodic POMDPs with unknown transition and observat…
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…