 The PIT leads to an implementable RL algorithm for the continuous-time MV problem. Our algorithm outperforms an adaptive control based method that estimates the underlying parameters in real-time and a state-of-the-art RL method that uses deep neural networks for continuous control problems by a large margin in nearly all simulations.
<coherence>  The PIT leads to an implementable RL algorithm for the continuous-time MV problem. Our algorithm outperforms an adaptive control based method that estimates the underlying parameters in real-time and a state-of-the-art RL method that uses deep neural networks for continuous control problems by a large margin in nearly all simulations.
 The PIT leads to an implementable RL algorithm . We find that our algorithm outperforms both an adaptive control based method that estimates the underlying parameters in real-time and a state-of-the-art RL method that uses deep neural networks for continuous control problems by a large margin in nearly all simulations.