Action Constrained Deep Reinforcement Learning Based Safe Automatic Driving Method

With the development of artificial intelligence,the field of autonomous driving is also growing.The deep reinforcement learning (DRL) method is one of the main research methods in this field.DRL algorithms have been reported to achieve excellent performance in many control tasks.However,the unconstrained exploration in the learning process of DRL usually restricts its application to automatic driving.For example,in common reinforcement learning (RL) algorithms,an agent often has to select an action to execute in each state although this action may result in a crash,deteriorating the performance,or even failing the task.To solve the problem,this paper proposes a new method of action constrained with the soft actor-critic algorithm (CSAC) where the 'NO-OP'(NO-Option) identifies and replaces inappropriate actions,and we test the algorithm in the lane-keeping tasks.The method firstly limits the environmental reward reasonably.When the rotation angle of the driverless car is too large,it will shake,then a penalty term will be added to the reward function to avoid the driverless car falling into a dangerous state as far as possible.The contributions of this paper are as follows:first,we incorporates action constrained function with SAC algorithm,which achieves faster learning speed and higher stability;second,we propose a reward setting framework that overcomes the shaking and instability of driverless cars,achieving a better performance;finally,we trains the model in the unity virtual environment for evaluating the performance and successfully transplant the model to a donkey driverless car