1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.RO2024
Offline-to-online Reinforcement Learning for Image-based Grasping with Scarce Demonstrations
Bryan Chan, Anson Leung, James Bergstra
Offline-to-online reinforcement learning (O2O RL) aims to obtain a continually improving policy as it interacts with the environment, while ensuring the initial policy behaviour is…
cs.RO2024★ 1 cited
Revisiting Sparse Rewards for Goal-Reaching Reinforcement Learning
Gautham Vasan, Yan Wang, Fahim Shahriar +3
Many real-world robot learning problems, such as pick-and-place or arriving at a destination, can be seen as a problem of reaching a goal state as soon as possible. These problems,…
cs.LG2023
A Statistical Guarantee for Representation Transfer in Multitask Imitation Learning
Bryan Chan, Karime Pereida, James Bergstra
Transferring representation for multitask imitation learning has the potential to provide improved sample efficiency on learning new tasks, when compared to learning from scratch.…