1 citations · 1 across the 2 of their papers we have counts for
2 papers
cs.LG2024★ 1 cited
Identifying Policy Gradient Subspaces
Jan Schneider, Pierre Schumacher, Simon Guist +4
Policy gradient methods hold great potential for solving complex continuous control tasks. Still, their training efficiency can be improved by exploiting structure within the optim…
cs.RO2023
Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning
Simon Guist, Jan Schneider, Alexander Dittrich +3
Reinforcement learning has shown great potential in solving complex tasks when large amounts of data can be generated with little effort. In robotics, one approach to generate trai…