Summary of Model-free Active Exploration in Reinforcement Learning, by Alessio Russo et al.
Model-Free Active Exploration in Reinforcement Learning
by Alessio Russo, Alexandre Proutiere
First submitted to arxiv on: 30 Jun 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 This paper presents a novel model-free solution for the problem of exploration in Reinforcement Learning, which involves identifying nearly-optimal policies. The authors adopt an information-theoretical viewpoint and derive an instance-specific lower bound on the number of samples required to identify such policies. This is achieved by solving an intricate optimization problem that requires a system model, which is typically estimated using existing sample optimal exploration algorithms. In contrast, this paper devises an ensemble-based model-free exploration strategy applicable to both tabular and continuous Markov decision processes. The proposed approach leverages an approximation of the instance-specific lower bound that only involves quantities inferable through model-free methods. Numerical results demonstrate that the strategy outperforms state-of-the-art exploration approaches in identifying efficient policies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists try to figure out how to explore and find good solutions in Reinforcement Learning, which is a type of machine learning. They come up with a new way to do this without needing a model of the system. This is important because most current methods rely on estimating the model, which can be difficult or slow. Instead, they develop an ensemble-based approach that uses only information that can be learned directly from the data. The results show that their method works better than other approaches in finding good solutions. |
Keywords
» Artificial intelligence » Machine learning » Optimization » Reinforcement learning
