Summary of Bigger, Regularized, Optimistic: Scaling For Compute and Sample-efficient Continuous Control, by Michal Nauman et al.
Bigger, Regularized, Optimistic: scaling for compute and sample-efficient continuous control
by Michal Nauman, Mateusz Ostaszewski, Krzysztof Jankowski, Piotr Miłoś, Marek Cygan
First submitted to arxiv on: 25 May 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 The proposed BRO (Bigger, Regularized, Optimistic) algorithm in this paper achieves state-of-the-art results by scaling model capacity and domain-specific Reinforcement Learning (RL) enhancements. By strong regularization of critic networks and optimistic exploration, BRO significantly outperforms leading model-based and model-free algorithms across 40 complex tasks from the DeepMind Control, MetaWorld, and MyoSuite benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows that scaling can be a powerful way to improve Reinforcement Learning (RL). The authors tested different ways of increasing the size of their models and found that this led to better results. They also developed a new algorithm called BRO that uses strong regularization and optimistic exploration to make decisions. This algorithm worked well across many different tasks, including some that are very hard to solve. |
Keywords
» Artificial intelligence » Regularization » Reinforcement learning
