Orthogonal Policy Gradient and Autonomous Driving Application

Abstract--One less addressed issue of deep reinforcement learning is the lack of generalization capability based on new state and new target, for complex tasks, it is necessary to give the correct strategy and evaluate all possible actions for current state. Fortunately, deep reinforcement learning has enabled enormous progress in both subproblems: giving the correct strategy and evaluating all actions based on the state. In this paper we present an approach called orthogonal policy gradient descent(OPGD) that can make agent learn the policy gradient based on the current state and the actions set, by which the agent can learn a policy network with generalization capability. The framework of the proposed method to implement the autonomous driving. In this paper we proposed a deep reinforcement learning(DRL) method called orthogonal policy gradient descent, which is prooved that the global optimization objective function can reach maximum value and is used in the application of autonomous driving.