28 citations · 44 across the 7 of their papers we have counts for
8 papers
Rethinking the Suitability of Reinforcement Learning Algorithms Under Practical Transfer Constraints
Hany Hamed, Abhishek Naik, Colin Bellinger +1
Transfer-oriented reinforcement learning requires evaluating algorithms along dimensions that go beyond standard sample efficiency. We focus on two dimensions: practical efficiency…
Benchmarking Action Spaces in Reinforcement Learning for Vision-based Robotic Manipulation
Seyed Alireza Azimi, Homayoon Farrahi, Abhishek Naik +2
In real-world reinforcement learning (RL), the choice of action space can play a key role in shaping motion smoothness, safety, and overall task performance. In this study, we eval…
Dynamic Object Masks as Goal Representations for Visual Goal-Conditioned Reinforcement Learning
Fahim Shahriar, Cheryl Wang, Alireza Azimi +5
Goal-conditioned reinforcement learning (GCRL) offers a unified way to pursue diverse tasks, yet most existing methods rely on state- or position-based goal representations that ar…
Average-Reward Learning and Planning with Options
Yi Wan, Abhishek Naik, Richard S. Sutton
We extend the options framework for temporal abstraction in reinforcement learning from discounted Markov decision processes (MDPs) to average-reward MDPs. Our contributions includ…
MADRaS : Multi Agent Driving Simulator
Anirban Santara, Sohan Rudra, Sree Aditya Buridi +4
In this work, we present MADRaS, an open-source multi-agent driving simulator for use in the design and evaluation of motion planning algorithms for autonomous driving. MADRaS prov…
Learning and Planning in Average-Reward Markov Decision Processes
Yi Wan, Abhishek Naik, Richard S. Sutton
We introduce learning and planning algorithms for average-reward MDPs, including 1) the first general proven-convergent off-policy model-free control algorithm without reference st…