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20222025
most citedNatural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models

10 citations · 26 across the 9 of their papers we have counts for

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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.LG2023

Investigating the Impact of Action Representations in Policy Gradient Algorithms

Jan Schneider, Pierre Schumacher, Daniel Häufle +2

Reinforcement learning~(RL) is a versatile framework for learning to solve complex real-world tasks. However, influences on the learning performance of RL algorithms are often poor…