activity
20222026
most citedOpen X-Embodiment: Robotic Learning Datasets and RT-X Models

103 citations · 106 across the 10 of their papers we have counts for

collaborators
Showing cs.LGShow all

2 papers · 1 filter

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…