8 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…
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…
Pure Exploration for Constrained Best Mixed Arm Identification with a Fixed Budget
Dengwang Tang, Rahul Jain, Ashutosh Nayyar +1
In this paper, we introduce the constrained best mixed arm identification (CBMAI) problem with a fixed budget. This is a pure exploration problem in a stochastic finite armed bandi…
Efficient Online Learning with Offline Datasets for Infinite Horizon MDPs: A Bayesian Approach
Dengwang Tang, Rahul Jain, Botao Hao +1
In this paper, we study the problem of efficient online reinforcement learning in the infinite horizon setting when there is an offline dataset to start with. We assume that the of…
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…