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.
What is the Unbalanced Atlas?
The Unbalanced Atlas is inspired by the manifold hypothesis—the idea that high-dimensional data often lies on lower-dimensional manifolds. Traditional manifold-based methods have shown promise but struggle to scale effectively in state-of-the-art self-supervised learning. UA addresses this by partitioning the encoding space unevenly, capturing complex data structures more flexibly and effectively than previous approaches.
Method & Innovation
Built upon the Spatiotemporal DeepInfomax (ST-DIM) framework, our method (DIM-UA) innovatively adapts the model to leverage the UA concept. This allows the model to learn richer, more informative state representations by aligning the learning process with the data’s intrinsic manifold structure.
Experimental Validation
We evaluated DIM-UA on the Atari Annotated RAM Interface benchmark—a challenging environment that provides annotated data for enhanced representation learning. DIM-UA outperformed existing methods, especially as the target encoding dimension increased. For example, with 16,384 hidden units, DIM-UA achieved an average F1 score of ~75%, surpassing ST-DIM’s ~70%.
Why Does This Matter?
Efficient and accurate state representations are crucial for advancing reinforcement learning agents and robots that learn and adapt in complex environments. By introducing UA, we open new paths to learn high-dimensional, structured data more effectively, pushing the boundaries of what self-supervised learning can achieve.
Looking Forward
This work lays a foundation for future research on manifold-based learning paradigms and their applications in reinforcement learning. We hope this inspires further exploration into flexible representation frameworks that better capture the true nature of complex data.