Summary of Random Latent Exploration For Deep Reinforcement Learning, by Srinath Mahankali et al.
Random Latent Exploration for Deep Reinforcement Learning
by Srinath Mahankali, Zhang-Wei Hong, Ayush Sekhari, Alexander Rakhlin, Pulkit Agrawal
First submitted to arxiv on: 18 Jul 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 introduces Random Latent Exploration (RLE), a new exploration strategy in reinforcement learning (RL) that outperforms traditional methods. RLE encourages the agent to explore different parts of the environment by pursuing randomly sampled goals in a latent space, making it as simple as noise-based methods while retaining the benefits of bonus-based exploration. The approach is demonstrated to improve performance on average in both discrete and continuous control tasks, enhancing exploration without requiring complex calculations. RLE can be easily plugged into existing RL algorithms, making it a promising solution for a range of applications. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to help computers learn by trying new things. It’s called Random Latent Exploration (RLE) and it works better than other methods that try to make the computer explore more. The idea is to give the computer random goals to work towards, which makes it look in different places for solutions. This helps the computer find better ways to solve problems without needing complicated calculations. The researchers tested RLE on different types of games and simulations and found that it worked well. |
Keywords
* Artificial intelligence * Latent space * Reinforcement learning
