10 citations · 11 across the 3 of their papers we have counts for
3 papers
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…
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…
Natural and Robust Walking using Reinforcement Learning without Demonstrations in High-Dimensional Musculoskeletal Models
Pierre Schumacher, Thomas Geijtenbeek, Vittorio Caggiano +4
Humans excel at robust bipedal walking in complex natural environments. In each step, they adequately tune the interaction of biomechanical muscle dynamics and neuronal signals to…