Reformulating DRL in a moving reference frame enables reliable control of rapid transitions between mode-locked states in a 1D RDE model by separating fast detonation propagation from slower operating-mode dynamics.
A Survey of Temporal Credit Assignment in Deep Reinforcement Learning.Transactions on Machine Learning Research, December 2023
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Timescale Separation Enables Deep Reinforcement Learning Control of Rotating Detonation Engine Mode Transitions
Reformulating DRL in a moving reference frame enables reliable control of rapid transitions between mode-locked states in a 1D RDE model by separating fast detonation propagation from slower operating-mode dynamics.