Search Results for author:
Found 4 papers, 3 papers with code
Robot Fine-Tuning Made Easy: Pre-Training Rewards and Policies for Autonomous Real-World Reinforcement Learning
We aim to enable this paradigm in robotic reinforcement learning, allowing a robot to learn a new task with little human effort by leveraging data and models from the Internet.
Offline Retraining for Online RL: Decoupled Policy Learning to Mitigate Exploration Bias
Can we leverage offline RL to recover better policies from online interaction?
Cal-QL: Calibrated Offline RL Pre-Training for Efficient Online Fine-Tuning
Our approach, calibrated Q-learning (Cal-QL), accomplishes this by learning a conservative value function initialization that underestimates the value of the learned policy from offline data, while also being calibrated, in the sense that the learned Q-values are at a reasonable scale.
Unsupervised Learning from Video with Deep Neural Embeddings
Because of the rich dynamical structure of videos and their ubiquity in everyday life, it is a natural idea that video data could serve as a powerful unsupervised learning signal for training visual representations in deep neural networks.
