Learning to Act without Actions: Recovering Latent Action Information from Videos

核心概念

Recovering latent action information from videos enables the training of powerful policies and world models without the need for action labels.

摘要

This content introduces Latent Action Policies (LAPO), a method for recovering latent action information purely from videos. LAPO enables the training of latent-action policies that can be fine-tuned into expert-level policies offline or online with rewards. The content is structured as follows:

1. Abstract

   • Pre-training large models on web data is effective.
   • Reinforcement learning lacks action labels in videos.
   • LAPO recovers latent action information from videos.

2. Introduction

   • Training on web-scale data is effective.
   • Common methods for learning policies require action labels.
   • LAPO infers latent-action information from videos.

3. Data Extraction

   • "Our experiments provide strong evidence that such latent policies accurately capture the observed expert’s behavior."
   • "LAPO does not rely on access to the true action space or a predefined set of discrete latent actions to learn a useful, pretrained policy."

4. Quotations

   • "LAPO is the first method able to recover the structure of the true action space just from observed dynamics."
   • "LAPO learns to infer latent actions, and consequently, obtain latent action policies in a fully unsupervised manner."

5. Experimental Setting

   • Uses the Procgen Benchmark for evaluation.
   • Observation-only dataset consists of 8M frames from an expert policy.
   • Implements IMPALA-CNN and U-Net for policy and IDM.

6. Results and Discussion

   • Decoding the latent policy online outperforms PPO from scratch.
   • Training a latent action decoder offline with a small dataset matches performance of training from scratch.
   • UMAP projection shows interpretable latent action spaces.

7. Conclusion

   • LAPO is a promising approach for obtaining pretrained policies from action-free demonstration data.
   • Future work aims to scale up LAPO for more complex domains.

統計資料

"Our experiments provide strong evidence that such latent policies accurately capture the observed expert’s behavior."
"LAPO does not rely on access to the true action space or a predefined set of discrete latent actions to learn a useful, pretrained policy."

引述

"LAPO is the first method able to recover the structure of the true action space just from observed dynamics."
"LAPO learns to infer latent actions, and consequently, obtain latent action policies in a fully unsupervised manner."

從以下內容提煉的關鍵洞見

Learning to Act without Actions

by Dominik Schm... 於 arxiv.org 03-28-2024

https://arxiv.org/pdf/2312.10812.pdf

深入探究

How can LAPO's approach be applied to other domains beyond reinforcement learning?

LAPO's approach of inferring latent actions purely from observational data can be applied to various domains beyond reinforcement learning. One potential application is in the field of autonomous driving. By analyzing videos of human drivers, LAPO could learn latent actions that correspond to safe and efficient driving behaviors. These latent actions could then be used to train autonomous vehicles to mimic human driving patterns, leading to more human-like and safer driving behavior.
Another application could be in healthcare, where LAPO could analyze videos of medical procedures to learn latent actions that represent optimal surgical techniques or patient care protocols. This information could then be used to train medical professionals or even robotic surgical systems to perform procedures more effectively and with better outcomes.
Additionally, in the field of robotics, LAPO could be used to analyze videos of robots performing tasks to learn latent actions that lead to successful task completion. This information could then be used to train new robots or improve existing ones by transferring the learned latent actions to different robotic systems.

What are the potential limitations of inferring latent actions purely from observational data?

While inferring latent actions purely from observational data has many advantages, there are also potential limitations to consider. One limitation is the challenge of capturing all relevant information from the observations to accurately infer the latent actions. In complex environments with high levels of stochasticity or partial observability, it may be difficult for the model to extract the necessary information to learn meaningful latent actions.
Another limitation is the potential for the latent actions to be context-dependent. In some situations, the same latent action may lead to different outcomes depending on the context in which it is applied. This can make it challenging to generalize the learned latent actions to new environments or tasks.
Additionally, inferring latent actions purely from observational data may not capture the full range of actions that are possible in a given environment. The model may be limited by the actions that are present in the observational data, leading to a narrow understanding of the action space.

How might LAPO's method impact the field of unsupervised pretraining in machine learning?

LAPO's method could have a significant impact on the field of unsupervised pretraining in machine learning by enabling the training of powerful, generalist policies and world models on massive amounts of observational data. By learning latent actions from videos without the need for action labels, LAPO opens up new possibilities for leveraging large-scale, unlabeled datasets in pretraining tasks.
One key impact of LAPO's method is the potential for more data-efficient pretraining. By learning latent actions and policies from observational data, LAPO reduces the reliance on labeled data, making it easier to scale up pretraining tasks to larger datasets. This could lead to the development of more robust and generalizable models across various domains.
Furthermore, LAPO's approach could facilitate the transfer of knowledge across tasks and domains. By pretraining policies on latent actions inferred from observational data, these policies could be fine-tuned on specific tasks with minimal labeled data, enabling rapid adaptation to new environments or tasks.
Overall, LAPO's method has the potential to revolutionize unsupervised pretraining in machine learning by leveraging the vast amounts of observational data available on the web to train powerful and adaptable models.