Summary of A Pontryagin Perspective on Reinforcement Learning, by Onno Eberhard et al.
A Pontryagin Perspective on Reinforcement Learning
by Onno Eberhard, Claire Vernade, Michael Muehlebach
First submitted to arxiv on: 28 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: 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 This paper introduces the concept of open-loop reinforcement learning, where a fixed action sequence is learned instead of state-dependent policies. The authors present three new algorithms: one robust model-based method and two sample-efficient model-free methods. These algorithms are based on Pontryagin’s principle from optimal control theory rather than Bellman’s equation from dynamic programming. The paper provides convergence guarantees and evaluates the performance of the algorithms empirically on a pendulum swing-up task and two high-dimensional MuJoCo tasks, achieving significant improvements over existing baselines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores a new way to learn using reinforcement learning. Instead of trying to figure out what actions to take in different situations, the goal is to learn a fixed sequence of actions that can be used again and again. The authors developed three new algorithms to solve this problem, which they tested on some tricky tasks like swinging up a pendulum and controlling robots. Their results show that these new methods work really well and could lead to better ways to control machines and robots in the future. |
Keywords
* Artificial intelligence * Reinforcement learning
