3 accepted papers
Policies learned via continuous actor-critic methods often exhibit erratic, high-frequency oscillations, making them unsuitable for physical deployment. Current approaches attempt to enforce smoothness by directly regularizing the policy's output. We argue that this approach treats the symptom rathe…
Reinforcement learning (RL) has proven its potential in complex decision-making tasks. Yet, many RL systems rely on manually crafted state representations, requiring effort in feature engineering. Visual Reinforcement Learning (VRL) offers a way to address this challenge by enabling agents to learn…
Visual Reinforcement Learning (RL) facilitates learning directly from raw images; however, the domain gap between training and testing environments frequently leads to a decline in performance within unseen environments. In this paper, we propose Fourier Guided Adaptive Adversarial Augmentation (FGA…