Summary of Towards Scalable General Utility Reinforcement Learning: Occupancy Approximation, Sample Complexity and Global Optimality, by Anas Barakat et al.
Towards Scalable General Utility Reinforcement Learning: Occupancy Approximation, Sample Complexity and Global Optimality
by Anas Barakat, Souradip Chakraborty, Peihong Yu, Pratap Tokekar, Amrit Singh Bedi
First submitted to arxiv on: 5 Oct 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 The paper proposes a unified reinforcement learning framework that addresses imitation learning, pure exploration, and safe reinforcement learning in large state-action spaces. By approximating occupancy measures using maximum likelihood estimation (MLE), the authors aim to improve upon previous work limited to the tabular setting. They introduce a simple policy gradient algorithm that updates policy parameters to maximize a general utility objective while estimating occupancy measures using MLE. The paper provides statistical complexity analysis, establishing first-order stationarity and global optimality performance bounds for nonconcave and concave general utilities. Empirical results show the scalability potential of their approach compared to existing tabular count-based methods. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about a new way to learn from experiences without making mistakes. It’s like having a super smart friend who helps you make good choices. The authors want to solve many problems at once, not just one or two. They use a special method to estimate how often things happen, which helps them make better decisions. This approach is useful when there are many possibilities and we need to learn from all of them. The paper shows that their method works well in practice and can be used for many different problems. |
Keywords
* Artificial intelligence * Likelihood * Reinforcement learning
