306 citations · 480 across the 9 of their papers we have counts for
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cs.LG2023
Parallel -Learning: Scaling Off-policy Reinforcement Learning under Massively Parallel Simulation
Zechu Li, Tao Chen, Zhang-Wei Hong +2
Reinforcement learning is time-consuming for complex tasks due to the need for large amounts of training data. Recent advances in GPU-based simulation, such as Isaac Gym, have sped…
cs.LG2022★ 4 cited
Offline RL Policies Should be Trained to be Adaptive
Dibya Ghosh, Anurag Ajay, Pulkit Agrawal +1
Offline RL algorithms must account for the fact that the dataset they are provided may leave many facets of the environment unknown. The most common way to approach this challenge…