1 citations · 2 across the 6 of their papers we have counts for
6 papers
The Trajectory Alignment Coefficient in Two Acts: From Reward Tuning to Reward Learning
Calarina Muslimani, Yunshu Du, Kenta Kawamoto +3
The success of reinforcement learning (RL) is fundamentally tied to having a reward function that accurately reflects the task objective. Yet, designing reward functions is notorio…
Reward Learning through Ranking Mean Squared Error
Chaitanya Kharyal, Calarina Muslimani, Matthew E. Taylor
Reward design remains a significant bottleneck in applying reinforcement learning (RL) to real-world problems. A popular alternative is reward learning, where reward functions are…
Towards Improving Reward Design in RL: A Reward Alignment Metric for RL Practitioners
Calarina Muslimani, Kerrick Johnstonbaugh, Suyog Chandramouli +3
Reinforcement learning agents are fundamentally limited by the quality of the reward functions they learn from, yet reward design is often overlooked under the assumption that a we…
Boosting Robustness in Preference-Based Reinforcement Learning with Dynamic Sparsity
Calarina Muslimani, Bram Grooten, Deepak Ranganatha Sastry Mamillapalli +3
To integrate into human-centered environments, autonomous agents must learn from and adapt to humans in their native settings. Preference-based reinforcement learning (PbRL) can en…
Leveraging Sub-Optimal Data for Human-in-the-Loop Reinforcement Learning
Calarina Muslimani, Matthew E. Taylor
To create useful reinforcement learning (RL) agents, step zero is to design a suitable reward function that captures the nuances of the task. However, reward engineering can be a d…
Reinforcement Teaching
Calarina Muslimani, Alex Lewandowski, Dale Schuurmans +2
Machine learning algorithms learn to solve a task, but are unable to improve their ability to learn. Meta-learning methods learn about machine learning algorithms and improve them…