Discrete space reinforcement learning algorithm based on support vector machine classification

When facing discrete space learning problems, the traditional reinforcement learning algorithms often have the problems of slow convergence and poor convergence accuracy. Deep reinforcement learning needs a large number of learning samples in its learning process, so it often faces with the problems that the algorithm is difficult to converge and easy to fall into local minimums. In view of the above problems, we apply support vector machines classification to reinforcement learning, and propose an algorithm named Advantage Actor-Critic with Support Vector Machine Classification (SVM-A2C). Our algorithm adopts the actor-critic framework and uses the support vector machine classification as a result of the actor's action output, while Critic uses the advantage function to improve and optimize the parameters of support vector machine. In addition, since the environment is changing all the time in reinforcement learning, it is difficult to find a global optimal solution for the support vector machines, the gradient descent method is applied to optimize the parameters of support vector machine. So that the agent can quickly learn a more precise action selection policy. Finally, the effectiveness of the proposed method is proved by the classical experimental environment of reinforcement learning. It is proved that the algorithm proposed in this paper has shorter episodes to convergence and more accurate results than other algorithms.