1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2025
Guided Flow Policy: Learning from High-Value Actions in Offline Reinforcement Learning
Franki Nguimatsia Tiofack, Théotime Le Hellard, Fabian Schramm +2
Offline reinforcement learning often relies on behavior regularization that enforces policies to remain close to the dataset distribution. However, such approaches fail to distingu…
cs.LG2023★ 1 cited
Rockmate: an Efficient, Fast, Automatic and Generic Tool for Re-materialization in PyTorch
Xunyi Zhao, Théotime Le Hellard, Lionel Eyraud +2
We propose Rockmate to control the memory requirements when training PyTorch DNN models. Rockmate is an automatic tool that starts from the model code and generates an equivalent m…