Agent learning method based on knowledge guidance-tactical perception

Abstract

The invention discloses an agent learning method based on knowledge guidance-tactical perception. In the environment of a two-party fight game, human knowledge is used to train an agent which fights with a specific tactical strategy through two stages of training. The method comprises the steps that 1) a game screen and the state of the agent are encoded to form a state feature; 2) a script with aspecific fight strategy is constructed by manually writing a script; 3) the agent fights with the script to collect the state and motion data of an opponent to carry out training in the first stage;4) the trained agent fights with a computer built-in AI, and training in the second stage is carried out through reinforcement learning; and 5) the learning framework is used to train the agent to participate in fight with specific tactics. The method provided by the invention is applicable to agent training in a two-party fight mode in a micro-operation environment, and acquires a good winning rate in the face of various micro-operation fight scenes.

Description

technical field [0001] The invention belongs to the application of deep reinforcement learning in the game field, and in particular relates to an agent learning method based on knowledge guidance-tactical perception. Background technique [0002] Deep reinforcement learning is applied in many places, and the more classic ones are in Go and Atari mini-games. Due to the complexity and real-time nature of the two-player game, after conquering the field of Go, researchers in reinforcement learning have turned to the study of two-player game agents such as StarCraft. Such research can not only strengthen the research on artificial intelligence, but also has high application significance. Because after the intelligent body is constructed, it can be applied to the game industry, put the intelligent body in the game, or apply it to the competitive sports industry, for training beginners or providing the best combat strategy, etc. [0003] Typically, the problems of agent research ...

AI Technical Summary

Problems solved by technology

Therefore, this reinforcement learning will always learn in the direction of the greatest return, which will also lead to a strange strategy in the end. Researchers cannot analyze what this strategy will be like in advance.

Moreover, since the previous methods are purely data-driven, during the training process of this method, the agent will not be aware of the existence of the opponent, which lacks the confrontation information of the game itself.

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