Seeking Visual Discomfort: Curiosity-driven Representations for Reinforcement Learning
Elie Aljalbout,Maximilian Ulmer,Rudolph Triebel,Elie Aljalbout,Maximilian Ulmer,Rudolph Triebel
Vision-based reinforcement learning (RL) is a promising approach to solve control tasks involving images as the main observation. State-of-the-art RL algorithms still struggle in terms of sample efficiency, especially when using image observations. This has led to increased attention on integrating state representation learning (SRL) techniques into the RL pipeline. Work in this field demonstrates...
