Summary of To Switch or Not to Switch? Balanced Policy Switching in Offline Reinforcement Learning, by Tao Ma et al.
To Switch or Not to Switch? Balanced Policy Switching in Offline Reinforcement Learning
by Tao Ma, Xuzhi Yang, Zoltan Szabo
First submitted to arxiv on: 1 Jul 2024
Categories
- Main: Machine Learning (stat.ML)
- Secondary: Information Theory (cs.IT); Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| 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 reinforcement learning (RL) method is introduced to balance the gain and cost of policy switching in offline scenarios, where data collection is limited. The algorithm, Net Actor-Critic, leverages optimal transport ideas to tackle this problem. It’s shown to be efficient on multiple robot control benchmarks and traffic light control tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to learn from past experiences without being able to interact with the environment further is developed. This approach helps balance the benefits of changing strategies with the costs of doing so. The method, Net Actor-Critic, is tested on various problems and performs well. |
Keywords
» Artificial intelligence » Reinforcement learning
