Summary of Towards Principled Representation Learning From Videos For Reinforcement Learning, by Dipendra Misra et al.
Towards Principled Representation Learning from Videos for Reinforcement Learning
by Dipendra Misra, Akanksha Saran, Tengyang Xie, Alex Lamb, John Langford
First submitted to arxiv on: 20 Mar 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV)
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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 investigates pre-training representations for decision-making using video data, which is abundant for tasks like game agents and software testing. While significant empirical progress has been made, a theoretical understanding remains lacking. The authors initiate a theoretical investigation into principled approaches for representation learning, focusing on learning latent state representations of underlying Markov Decision Processes (MDPs) using video data. They study two settings: one with independent identically distributed (i.i.d.) noise and another with exogenous non-i.i.d. noise like motion in the background. The authors analyze three common methods: autoencoding, temporal contrastive learning, and forward modeling. They prove upper bounds for temporal contrastive learning and forward modeling in the presence of only i.i.d. noise, showing these approaches can learn latent states and use them for efficient downstream reinforcement learning with polynomial sample complexity. When exogenous noise is present, they establish a lower bound result indicating that video-based learning can be exponentially worse than action-labeled trajectory data. This partially explains why reinforcement learning with video pre-training is challenging. The authors evaluate representational learning methods in two visual domains, yielding results consistent with their theoretical findings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper looks at how to use video data to help make decisions, which is important for things like game agents and software testing. While some progress has been made, we don’t really understand why it works. The authors try to figure out the rules behind this process, focusing on using video data to learn about underlying processes. They test three different methods: autoencoding, temporal contrastive learning, and forward modeling. They find that these methods can work well when there’s just a little bit of noise in the system, but it gets much harder when there’s more complex noise like people or cars moving around. |
Keywords
* Artificial intelligence * Reinforcement learning * Representation learning
