Summary of Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy Networks, by Annie Wong et al.
Solving Deep Reinforcement Learning Tasks with Evolution Strategies and Linear Policy Networks
by Annie Wong, Jacob de Nobel, Thomas Bäck, Aske Plaat, Anna V. Kononova
First submitted to arxiv on: 10 Feb 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 Medium Difficulty summary: This study compares Evolution Strategies (ES) to gradient-based deep reinforcement learning methods for optimizing neural network weights via neuroevolution. The authors evaluate ES against three classical ES and Augmented Random Search, all using linear policy networks, on various benchmark tasks. Surprisingly, ES finds effective policies for many tasks, outperforming larger neural networks. While ES achieves comparable results to gradient-based deep reinforcement learning algorithms for more complex tasks, it surpasses Deep Q-Learning in Atari games by directly accessing the memory state. This suggests that current benchmarks may be easier to solve than assumed, and ES shows superior sample efficiency and robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Low Difficulty summary: Researchers compared two ways of improving artificial intelligence (AI) learning. They tested a method called Evolution Strategies, which is different from more common AI approaches. The results showed that Evolution Strategies can learn quickly and effectively for many tasks. This is surprising because current benchmarks assume these tasks are harder to solve than they actually are. In some cases, Evolution Strategies even outperformed other AI methods. The study also found that Evolution Strategies can learn better when it has direct access to the information it needs. |
Keywords
* Artificial intelligence * Neural network * Reinforcement learning
