Summary of Action Abstractions For Amortized Sampling, by Oussama Boussif et al.
Action abstractions for amortized sampling
by Oussama Boussif, Léna Néhale Ezzine, Joseph D Viviano, Michał Koziarski, Moksh Jain, Nikolay Malkin, Emmanuel Bengio, Rim Assouel, Yoshua Bengio
First submitted to arxiv on: 19 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 This paper addresses challenges in reinforcement learning (RL) and generative flow networks (GFlowNets) when dealing with long planning horizons. As policies grow longer, credit assignment and exploration become more difficult, hindering mode discovery and generalization. The authors propose an approach to incorporate action abstraction into the policy optimization process, iteratively extracting common subsequences of high-reward trajectories and ‘chunking’ them into single actions. This method demonstrates improved sample efficiency in discovering diverse high-reward objects, particularly on harder exploration problems, while also providing interpretable, high-order actions that capture the reward landscape’s latent structure. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you’re trying to find the best route to a treasure chest, but it takes many steps to get there. This can be really hard! The authors of this paper have found a way to make it easier by breaking down big actions into smaller ones that are more like steps on the path. They tested this idea and showed that it works better than usual methods when trying to find many different treasures, especially when it’s hard to explore. This new approach helps us understand what we’re doing as we search for treasure (or anything else!). |
Keywords
» Artificial intelligence » Generalization » Optimization » Reinforcement learning
