1 citations · 1 across the 7 of their papers we have counts for
7 papers
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…
Learning Without Time-Based Embodiment Resets in Soft-Actor Critic
Homayoon Farrahi, A. Rupam Mahmood
When creating new reinforcement learning tasks, practitioners often accelerate the learning process by incorporating into the task several accessory components, such as breaking th…
Revisiting Scalable Hessian Diagonal Approximations for Applications in Reinforcement Learning
Mohamed Elsayed, Homayoon Farrahi, Felix Dangel +1
Second-order information is valuable for many applications but challenging to compute. Several works focus on computing or approximating Hessian diagonals, but even this simplifica…
Analysis of Marketed versus Not-marketed Mobile App Releases
Maleknaz Nayebi, Homayoon Farrahi, Guenther Ruhe
Market and user characteristics of mobile apps make their release management different from proprietary software products and web services. Despite the wealth of information regard…
More Insight from Being More Focused: Analysis of Clustered Market Apps
Maleknaz Nayebi, Homayoon Farrahi, Ada Lee +2
The increasing attraction of mobile apps has inspired researchers to analyze apps from different perspectives. As with any software product, apps have different attributes such as…
Reducing the Cost of Cycle-Time Tuning for Real-World Policy Optimization
Homayoon Farrahi, A. Rupam Mahmood
Continuous-time reinforcement learning tasks commonly use discrete steps of fixed cycle times for actions. As practitioners need to choose the action-cycle time for a given task, a…
Model-free Policy Learning with Reward Gradients
Qingfeng Lan, Samuele Tosatto, Homayoon Farrahi +1
Despite the increasing popularity of policy gradient methods, they are yet to be widely utilized in sample-scarce applications, such as robotics. The sample efficiency could be imp…