Summary of Exploration Implies Data Augmentation: Reachability and Generalisation in Contextual Mdps, by Max Weltevrede et al.
Exploration Implies Data Augmentation: Reachability and Generalisation in Contextual MDPs
by Max Weltevrede, Caroline Horsch, Matthijs T.J. Spaan, Wendelin Böhmer
First submitted to arxiv on: 4 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 The paper investigates the zero-shot policy transfer (ZSPT) setting for contextual Markov decision processes (MDP), where agents must generalize to new contexts after training on fixed ones. Recent work suggests that increased exploration can improve this generalization by exposing agents to more states in the training contexts. This paper shows that while training on more states does improve generalization, it comes at a cost of reduced value function accuracy. The authors introduce reachability as a metric to define which states/contexts require generalization and demonstrate how exploration can improve it. They propose Explore-Go, an algorithm combining exploration and existing RL methods, and show its effectiveness in improving generalization in partially observable MDPs. The paper’s contributions include a new metric for evaluating generalization and a simple modification (Explore-Go) that practitioners can use to improve their agents’ generalization performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about helping AI learn to adapt to new situations without being trained on those exact situations before. Right now, AI learns by practicing in different environments, but it’s not great at adapting to completely new ones. The authors found that if they let the AI explore more during training, it gets better at adapting to new situations. They also introduced a new way to measure how well an AI generalizes and showed that their method works even when the situation is partially hidden from view. This could help people create better AI agents in the future. |
Keywords
» Artificial intelligence » Generalization » Zero shot
