From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments

In three linesTheoretical framework for deep reinforcement learning in continuous environments modeled as continuous-time stochastic processes. For single-hidden-layer networks, authors characterize state distribution evolution via stochastic differential equations in the infinite width limit.

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