Summary of Kalmamba: Towards Efficient Probabilistic State Space Models For Rl Under Uncertainty, by Philipp Becker et al.
KalMamba: Towards Efficient Probabilistic State Space Models for RL under Uncertainty
by Philipp Becker, Niklas Freymuth, Gerhard Neumann
First submitted to arxiv on: 21 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Machine Learning (stat.ML)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed KalMamba architecture learns representations for Reinforcement Learning (RL) by combining the strengths of probabilistic State Space Models (SSMs) and their deterministic counterparts. It leverages Mamba to learn dynamics parameters in a latent space, allowing for efficient inference using standard Kalman filtering and smoothing. The parallel associative scanning approach enables principled, highly efficient, and scalable probabilistic SSMs that compete with state-of-the-art approaches in RL while improving computational efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary KalMamba is a new way to learn representations for Reinforcement Learning (RL) that combines the best of both worlds – probabilistic State Space Models (SSMs) and their deterministic counterparts. It’s like a superpower for robots or AI systems that can make decisions based on incomplete information! The idea is to use Mamba, which is already really good at learning dynamics parameters, to help create a new type of SSM that’s fast and efficient. This means it can learn from more data and make better decisions faster. |
Keywords
» Artificial intelligence » Inference » Latent space » Reinforcement learning
