Abstract: This paper proposes an integrated guidance and control algorithm based on deep reinforcement learning technique. Differently from the traditional integrated guidance and control algorithm and designing the guidance loop and control loop separately, the fin deflection command of proposed integrated guidance and control algorithm is given by the agent through the observation states of missile. The agent is generated by the deep reinforcement learning. To utilize the deep reinforcement learning technique in integrated guidance and control problem, we transfer the integrated guidance and control problem into a Markovian decision process that enables the application of reinforcement learning theory. A heuristic way is utilized to shape a proper reward function that has tradeoff between guidance accuracy, energy consumption and interception time. The state of the art deep deterministic policy gradient algorithm is utilized to learn an action policy that maps the observation states to a fin deflection command. Extensive empirical numerical simulations are performed to validate the effectiveness and robustness of proposed integrated guidance and control algorithm.

Key words: Integrated guidance and control, Deep reinforcement learning, Deep deterministic policy gradient, Zero effort miss, Heuristic learning