Summary of Sample and Oracle Efficient Reinforcement Learning For Mdps with Linearly-realizable Value Functions, by Zakaria Mhammedi
Sample and Oracle Efficient Reinforcement Learning for MDPs with Linearly-Realizable Value Functions
by Zakaria Mhammedi
First submitted to arxiv on: 7 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 presents an efficient algorithm for Markov Decision Processes (MDPs) where the state-action value function of any policy is linear in a given feature map. The proposed algorithm efficiently finds a near-optimal policy in environments with large or infinite state and action spaces, using a number of episodes and calls to a cost-sensitive classification (CSC) oracle that are both polynomial in the problem parameters. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists developed an innovative way to improve reinforcement learning (RL) algorithms. They created an algorithm for Markov Decision Processes (MDPs) with large or infinite state and action spaces. This is important because it helps us find near-optimal solutions more efficiently. The new algorithm uses a special tool called a cost-sensitive classification (CSC) oracle, which can be used to make decisions in these complex environments. |
Keywords
» Artificial intelligence » Classification » Feature map » Reinforcement learning
