Reinforcement learning identifies optimal closed-loop feedback strategies in an overdamped colloidal optical trap that enable work extraction by fluctuation exploitation, matching exact theory and extending to heterogeneous forcing.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cond-mat.stat-mech 1years
2026 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
Work Extraction via Backward Motion in Optimal Closed-Loop Stochastic Control
Reinforcement learning identifies optimal closed-loop feedback strategies in an overdamped colloidal optical trap that enable work extraction by fluctuation exploitation, matching exact theory and extending to heterogeneous forcing.