Summary of Learning a Fast Mixing Exogenous Block Mdp Using a Single Trajectory, by Alexander Levine et al.
Learning a Fast Mixing Exogenous Block MDP using a Single Trajectory
by Alexander Levine, Peter Stone, Amy Zhang
First submitted to arxiv on: 3 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 paper proposes an efficient unsupervised representation learning framework for sequential decision-making environments, particularly important for training agents that can adapt quickly to new objectives or reward functions. The Exogenous Block Markov Decision Process (Ex-BMDP) formalizes this problem by decomposing high-dimensional observations into two latent factors: controllable and exogenous. The goal is to learn an encoder mapping from observations to the controllable latent space, as well as its dynamics. Prior work has shown that this is possible with sample complexity dependent only on the controllable latent space size, not the noise factor size. However, existing methods focus on episodic settings where the controllable state resets after a finite horizon. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us understand how to train better agents that can quickly adapt to new situations. It proposes a new way of learning from observations in decision-making environments. The goal is to find patterns in what we observe and use those patterns to make good decisions. This is important because it allows our agents to learn faster and do better in complex, changing situations. |
Keywords
» Artificial intelligence » Encoder » Latent space » Representation learning » Unsupervised
