3 accepted papers
Offline reinforcement learning (Offline RL) provides a compelling solution for applying RL in high-risk or resourceconstrained real-world domains such as healthcare, autonomous driving, and robotic manipulation, where online exploration can be unsafe or impractical. However, Offline RL faces critica
Offline reinforcement learning (Offline RL) provides a compelling solution for applying RL in high-risk or resource-constrained real-world domains such as healthcare, autonomous driving, and robotic manipulation. However, Offline RL faces critical challenges arising from limited data coverage and po…
Deep reinforcement learning has achieved great success in many challenging domains. However, sample efficiency and safety issues still prevent from applying deep reinforcement learning directly in robotics. Sim-to-real transfer learning is one feasible solution to tackle these problems and address t