Abstract: Aiming at the problems of high input dimensionality and long training time in traditional game intelligent  algorithm models, we  proposed a novel deep reinforcement learning game intelligent  guidance algorithm that integrated state information transformation and reward function shaping techniques. Firstly, using  the interface provided by the Unity engine to directly read game backend  information effectively compressed  the dimensionality of the state space and reduced the amount of input data. Secondly, by finely designing  the reward mechanism, the convergence process of the model was accelerated. Finally, we conducted comparative experiments between the proposed algorithm model and existing methods  from both subjective qualitative and objective quantitative perspectives. The experimental results show that this algorithm not only significantly improves the training efficiency of the model,  but also markedly enhances the performance of the  agent.

Key words: deep reinforcement learning, game agent, reward function shaping, proximal policy optimization algorithm