Summary of The Role Of Deep Learning Regularizations on Actors in Offline Rl, by Denis Tarasov et al.
The Role of Deep Learning Regularizations on Actors in Offline RL
by Denis Tarasov, Anja Surina, Caglar Gulcehre
First submitted to arxiv on: 11 Sep 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 This paper addresses the limitation of deep learning regularization techniques in Reinforcement Learning (RL), particularly in offline RL settings. Standard techniques like dropout, layer normalization, or weight decay are often used to improve model robustness and generalization capabilities in neural networks. However, these methods have been underexplored in RL, especially when applied to actor networks in actor-critic algorithms. The study empirically shows that applying these regularization techniques to actor networks improves performance by 6% on average across two algorithms and three D4RL domains. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how to make artificial intelligence (AI) better at learning from past experiences. Right now, AI has a hard time using what it learned before to improve its future decisions. The researchers found that using special techniques to make the AI’s “brain” more robust and flexible can really help it learn faster and make better choices. They tested this idea on three different types of games and found that it works! |
Keywords
» Artificial intelligence » Deep learning » Dropout » Generalization » Regularization » Reinforcement learning
