Contrastive Initial State Buffer for Reinforcement Learning
Nico Messikommer,Yunlong Song,Davide Scaramuzza,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 have been effective in leveraging past experiences for policy updates, they often overlook the potential of reusing past experiences for data collection. Independent of the underlying RL algorithm, we introduce the concept...
