5 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 +6
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…
Versatile and Generalizable Manipulation via Goal-Conditioned Reinforcement Learning with Grounded Object Detection
Huiyi Wang, Fahim Shahriar, Alireza Azimi +3
General-purpose robotic manipulation, including reach and grasp, is essential for deployment into households and workspaces involving diverse and evolving tasks. Recent advances pr…
Deep Policy Gradient Methods Without Batch Updates, Target Networks, or Replay Buffers
Gautham Vasan, Mohamed Elsayed, Alireza Azimi +5
Modern deep policy gradient methods achieve effective performance on simulated robotic tasks, but they all require large replay buffers or expensive batch updates, or both, making…