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Optimal steering of a smart active particle
Soft Condensed Matter · Submitted 7 Sep 2019
abstract compiled · 2026-09-23 · no language-model review
The authors' main claim The optimal steering is stored in the optimized action-value function, which is able to rectify thermal fluctuations.
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The abstract, sentence by sentence
We formulate the theory for steering an active particle with optimal travel time between two locations and apply it to the Mexican hat potential without brim. For small heights the particle can cross the potential barrier, while for large heights it has to move around it. Thermal fluctuations in the orientation strongly affect the path over the barrier. Then we consider a smart active particle and apply reinforcement learning. We show how the active particle learns in repeating episodes to move optimally. The optimal steering is stored in the optimized action-value function, which is able to rectify thermal fluctuations.
background contribution method result interpretation main claim
| # | sentence | role | the authors' own work | states the main conclusion | strength |
|---|---|---|---|---|---|
| s0 | We formulate the theory for steering an active particle with optimal travel time between two locations and apply it to the Mexican hat potential without brim. | contribution 0.92 | 0.93 | 0.10 | asserted 0.99 |
| s1 | For small heights the particle can cross the potential barrier, while for large heights it has to move around it. | result 0.99 | 0.78 | 0.11 | asserted 0.99 |
| s2 | Thermal fluctuations in the orientation strongly affect the path over the barrier. | result 1.00 | 0.78 | 0.09 | asserted 1.00 |
| s3 | Then we consider a smart active particle and apply reinforcement learning. | method 0.53 | 0.92 | 0.04 | asserted 0.74 |
| s4 | We show how the active particle learns in repeating episodes to move optimally. | method 0.42 | 0.91 | 0.17 | asserted 1.00 |
| s5 | The optimal steering is stored in the optimized action-value function, which is able to rectify thermal fluctuations. | result 0.88 | 0.91 | 0.49 | asserted 1.00 |
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The paper
Original abstract
We formulate the theory for steering an active particle with optimal travel time between two locations and apply it to the Mexican hat potential without brim. For small heights the particle can cross the potential barrier, while for large heights it has to move around it. Thermal fluctuations in the orientation strongly affect the path over the barrier. Then we consider a smart active particle and apply reinforcement learning. We show how the active particle learns in repeating episodes to move optimally. The optimal steering is stored in the optimized action-value function, which is able to rectify thermal fluctuations.
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3 arXiv papers cite this in 4 sentences
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Furthermore, we compare RL with solutions from Optimal Navigation (ON) theory [ref] and show that the latter is of almost no practical use for the case of navigation in turbulent flows due to strong sensitivity to the initial (and final) conditions, in contrast to what happens for simpler advecting flows [ref] .
Optimal control of point-to-point navigation in turbulent time-dependent flows using Reinforcement Learning · Introduction · 0.95
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