Unmanned lane keeping method based on maximum entropy reinforcement learning framework

The invention discloses an unmanned lane keeping method based on a maximum entropy reinforcement learning framework. The unmanned lane keeping method comprises the following steps: (1) creating a unmanned vehicle simulation road environment; setting an environment vehicle driving strategy and a pedestrian motion model, and designing a reward function and a collision detection condition; (2) approximating a state value function, an action value function and a strategy by using a deep neural network, and initializing network parameters; (3) obtaining the initial state of the unmanned vehicle, enabling the unmanned vehicle to interact with the environment, collecting data, and storing the data in a buffer pool; (4) updating the state value function network, the action value function network and the strategy network; (5) updating the target value function network until the strategy network is close to convergence; (6) carrying out zero setting on an entropy item coefficient in a state value network optimization t