3 citations · 6 across the 8 of their papers we have counts for
5 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…
Monitored Markov Decision Processes
Simone Parisi, Montaser Mohammedalamen, Alireza Kazemipour +2
In reinforcement learning (RL), an agent learns to perform a task by interacting with an environment and receiving feedback (a numerical reward) for its actions. However, the assum…
LaFFi: Leveraging Hybrid Natural Language Feedback for Fine-tuning Language Models
Qianxi Li, Yingyue Cao, Jikun Kang +4
Fine-tuning Large Language Models (LLMs) adapts a trained model to specific downstream tasks, significantly improving task-specific performance. Supervised Fine-Tuning (SFT) is a c…
Learning from Multiple Independent Advisors in Multi-agent Reinforcement Learning
Sriram Ganapathi Subramanian, Matthew E. Taylor, Kate Larson +1
Multi-agent reinforcement learning typically suffers from the problem of sample inefficiency, where learning suitable policies involves the use of many data samples. Learning from…
Automatic Noise Filtering with Dynamic Sparse Training in Deep Reinforcement Learning
Bram Grooten, Ghada Sokar, Shibhansh Dohare +4
Tomorrow's robots will need to distinguish useful information from noise when performing different tasks. A household robot for instance may continuously receive a plethora of info…