Summary of Koopman-assisted Reinforcement Learning, by Preston Rozwood et al.
Koopman-Assisted Reinforcement Learning
by Preston Rozwood, Edward Mehrez, Ludger Paehler, Wen Sun, Steven L. Brunton
First submitted to arxiv on: 4 Mar 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Machine Learning (cs.LG); Dynamical Systems (math.DS); Optimization and Control (math.OC)
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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 explores the connection between data-driven Koopman operators and Markov Decision Processes (MDPs) to develop two new reinforcement learning (RL) algorithms that address limitations in Bellman equation-based methods. The Koopman operator is used to lift nonlinear systems into linear coordinates, enabling HJB-based methods to be more tractable. This leads to the development of “Koopman tensor” and reformulation of max-entropy RL algorithms: soft value iteration and soft actor-critic (SAC). These Koopman Assisted Reinforcement Learning (KARL) algorithms achieve state-of-the-art performance on four controlled dynamical systems, outperforming traditional neural network-based SAC and linear quadratic regulator (LQR) baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper uses mathematical equations to help robots learn how to make good decisions. They use a special tool called the Koopman operator to turn complicated problems into simpler ones that can be solved more easily. This helps them develop new ways for robots to learn and make decisions, which is important because it allows them to do things like play games or navigate through tricky spaces. |
Keywords
* Artificial intelligence * Neural network * Reinforcement learning
