2 papers
cs.RO2025
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
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,…