3 citations · 5 across the 4 of their papers we have counts for
4 papers
MOTO: Offline Pre-training to Online Fine-tuning for Model-based Robot Learning
Rafael Rafailov, Kyle Hatch, Victor Kolev +3
We study the problem of offline pre-training and online fine-tuning for reinforcement learning from high-dimensional observations in the context of realistic robot tasks. Recent of…
Searching for High-Value Molecules Using Reinforcement Learning and Transformers
Raj Ghugare, Santiago Miret, Adriana Hugessen +2
Reinforcement learning (RL) over text representations can be effective for finding high-value policies that can search over graphs. However, RL requires careful structuring of the…
Learning Sparse Control Tasks from Pixels by Latent Nearest-Neighbor-Guided Explorations
Ruihan Zhao, Ufuk Topcu, Sandeep Chinchali +1
Recent progress in deep reinforcement learning (RL) and computer vision enables artificial agents to solve complex tasks, including locomotion, manipulation and video games from hi…
DNS: Determinantal Point Process Based Neural Network Sampler for Ensemble Reinforcement Learning
Hassam Sheikh, Kizza Frisbee, Mariano Phielipp
Application of ensemble of neural networks is becoming an imminent tool for advancing the state-of-the-art in deep reinforcement learning algorithms. However, training these large…