Derives an SDE describing the infinitesimal change in state distribution at each gradient step for neural actor-critic RL in continuous environments under vanishing learning rate in the infinite width limit.
arXiv preprint arXiv:2407.17226 , year=
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From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments
Derives an SDE describing the infinitesimal change in state distribution at each gradient step for neural actor-critic RL in continuous environments under vanishing learning rate in the infinite width limit.