2 papers
cs.LG2025
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…
cs.LG2025
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…