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

From Ticks to Flows: Dynamics of Neural Reinforcement Learning in Continuous Environments arXiv:2606.04275v1 Announce Type: cross Abstract: We present a novel theoretical framework for deep reinforcement learning (RL) in continuous environments by modeling the problem as a continuous-time stochastic process, drawing on insights from stochastic control. Building on previous work, we introduce a viable model of actor-critic algorithm that incorporates both exploration and stochastic transitions.