2 citations · 3 across the 5 of their papers we have counts for
7 papers
RoboFQE: Rollout-Free Evaluation of Visual Robot Policies from Offline Trajectories
Yunfu Deng, Sangwoo Shin, Brahma S. Pavse +1
Evaluating robot policies before deployment typically requires producing new trajectories through real-world execution, simulators, or learned world models. These rollout-based app…
Stable Offline Value Function Learning with Bisimulation-based Representations
Brahma S. Pavse, Yudong Chen, Qiaomin Xie +1
In reinforcement learning, offline value function learning is the procedure of using an offline dataset to estimate the expected discounted return from each state when taking actio…
State-Action Similarity-Based Representations for Off-Policy Evaluation
Brahma S. Pavse, Josiah P. Hanna
In reinforcement learning, off-policy evaluation (OPE) is the problem of estimating the expected return of an evaluation policy given a fixed dataset that was collected by running…
Learning to Stabilize Online Reinforcement Learning in Unbounded State Spaces
Brahma S. Pavse, Matthew Zurek, Yudong Chen +2
In many reinforcement learning (RL) applications, we want policies that reach desired states and then keep the controlled system within an acceptable region around the desired stat…
Scaling Marginalized Importance Sampling to High-Dimensional State-Spaces via State Abstraction
Brahma S. Pavse, Josiah P. Hanna
We consider the problem of off-policy evaluation (OPE) in reinforcement learning (RL), where the goal is to estimate the performance of an evaluation policy, , using a fixed d…
Reducing Sampling Error in Batch Temporal Difference Learning
Brahma Pavse, Ishan Durugkar, Josiah Hanna +1
Temporal difference (TD) learning is one of the main foundations of modern reinforcement learning. This paper studies the use of TD(0), a canonical TD algorithm, to estimate the va…
RIDM: Reinforced Inverse Dynamics Modeling for Learning from a Single Observed Demonstration
Brahma S. Pavse, Faraz Torabi, Josiah P. Hanna +2
Augmenting reinforcement learning with imitation learning is often hailed as a method by which to improve upon learning from scratch. However, most existing methods for integrating…