State Representation Learning Using an Unbalanced Atlas
Representation learning is a key challenge in artificial intelligence, aiming to find efficient ways to represent high-dimensional data for downstream tasks like reinforcement learning and robotics. In our recent work, we introduce a novel self-supervised learning paradigm called the Unbalanced Atlas (UA), which significantly advances state representation learning for reinforcement learning.
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