4 papers · 1 filter
CANDERE-COACH: Reinforcement Learning from Noisy Feedback
Yuxuan Li, Srijita Das, Matthew E. Taylor
In recent times, Reinforcement learning (RL) has been widely applied to many challenging tasks. However, in order to perform well, it requires access to a good reward function whic…
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…
MaDi: Learning to Mask Distractions for Generalization in Visual Deep Reinforcement Learning
Bram Grooten, Tristan Tomilin, Gautham Vasan +5
The visual world provides an abundance of information, but many input pixels received by agents often contain distracting stimuli. Autonomous agents need the ability to distinguish…