Summary of Computationally Efficient Rl Under Linear Bellman Completeness For Deterministic Dynamics, by Runzhe Wu et al.
Computationally Efficient RL under Linear Bellman Completeness for Deterministic Dynamics
by Runzhe Wu, Ayush Sekhari, Akshay Krishnamurthy, Wen Sun
First submitted to arxiv on: 17 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Robotics (cs.RO); Systems and Control (eess.SY)
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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 computationally efficient Reinforcement Learning (RL) algorithms for the linear Bellman Complete setting, which combines existing models like Linear Markov Decision Processes (MDPs) and Linear Quadratic Regulators (LQRs). The algorithm is based on randomization and optimistically iterates over value functions using least squares regression. It can handle large action spaces, random initial states, and rewards while relying on deterministic dynamics. The key innovation lies in carefully injecting noise into the regression problems to ensure optimism without amplifying errors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes Reinforcement Learning more efficient by finding a new way to solve a special kind of problem called the linear Bellman Complete setting. This setting is important because it helps us understand how to make decisions when there are many possible actions and the outcome depends on those actions. The new algorithm uses random noise in a clever way to avoid mistakes and make the process more efficient. It’s especially useful when we don’t know what will happen next, but the underlying rules stay the same. |
Keywords
* Artificial intelligence * Regression * Reinforcement learning
