#stochastic-processes News & Analysis

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

Researchers present a theoretical framework for deep reinforcement learning in continuous environments using continuous-time stochastic processes and stochastic control theory. The work establishes a two time-scale model for actor-critic algorithms with neural networks, deriving equations that describe how state distributions evolve during training in the infinite width limit.