223 citations · 273 across the 42 of their papers we have counts for
Showing cs.LGShow all
2 papers · 1 filter
cs.LG2026
Adaptive Expert Guidance for Efficient On-Policy Reinforcement Learning
Daniele Affinita, Ming Xu, Rudolf Reiter +2
With massively parallel simulation, on-policy Reinforcement Learning methods such as PPO have become standard in many domains. However, learning from scratch is sample-inefficient…
cs.LG2023
Contrastive Initial State Buffer for Reinforcement Learning
Nico Messikommer, Yunlong Song, Davide Scaramuzza
In Reinforcement Learning, the trade-off between exploration and exploitation poses a complex challenge for achieving efficient learning from limited samples. While recent works ha…