Summary of Explore-go: Leveraging Exploration For Generalisation in Deep Reinforcement Learning, by Max Weltevrede et al.
Explore-Go: Leveraging Exploration for Generalisation in Deep Reinforcement Learning
by Max Weltevrede, Felix Kaubek, Matthijs T.J. Spaan, Wendelin Böhmer
First submitted to arxiv on: 12 Jun 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 A novel approach to reinforcement learning, called Explore-Go, is introduced to improve agents’ ability to generalize to novel scenarios. The method leverages increased exploration during training to enhance generalization performance, even when states encountered during testing cannot be explicitly trained on. This is achieved by increasing the starting state distribution of the agent, making it compatible with most existing reinforcement learning algorithms. Empirical results demonstrate improved generalization performance in an illustrative environment and on the Procgen benchmark. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Reinforcement learning agents can learn to solve new problems by training on a variety of tasks. Researchers have found that when they explore more during training, their performance improves. This makes sense if the agent encounters similar situations during testing as it did during training. But what if the situation is completely new? A new approach called Explore-Go helps agents do better in these situations too. It works by giving them a broader range of starting points to start from. This can be used with many existing methods and has been shown to work well in certain environments. |
Keywords
» Artificial intelligence » Generalization » Reinforcement learning
