Summary of Do Agents Dream Of Electric Sheep?: Improving Generalization in Reinforcement Learning Through Generative Learning, by Giorgio Franceschelli and Mirco Musolesi
Do Agents Dream of Electric Sheep?: Improving Generalization in Reinforcement Learning through Generative Learning
by Giorgio Franceschelli, Mirco Musolesi
First submitted to arxiv on: 12 Mar 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 explores the Overfitted Brain hypothesis’ applicability to reinforcement learning agents. It proposes an imagination-based approach, utilizing generative augmentations to modify predicted trajectories in dream-like episodes. The authors compare this method to classic imagination and offline training on collected experience, demonstrating improved generalization in sparsely rewarded ProcGen environments. Specifically, the paper trains policies using imagination-based reinforcement learning and evaluates their performance on four ProcGen benchmarks. The results show that this approach can lead to better generalization capabilities compared to traditional methods. By leveraging imagination and generative augmentations, the authors provide a framework for improving generalization in reinforcement learning agents. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how computer programs (reinforcement learning agents) can learn new skills when they don’t have much experience. The researchers propose a way to use imaginary scenarios to help these agents learn and adapt better. They tested their method on four different environments and found that it performed better than other approaches in situations where rewards were scarce. This could lead to improvements in how AI systems learn and generalize from limited experience. |
Keywords
* Artificial intelligence * Generalization * Reinforcement learning
