Reinforcement learning battle game AI training method based on information bottleneck theory

A technology of reinforcement learning and information bottleneck, which is applied in the field of game intelligent AI learning, can solve problems such as single routine, reduce mutual information, and lack of flexibility in fighting between players, so as to save training time, speed up training, and improve sampling efficiency effect
CN112717415AActive Publication Date: 2021-04-30SHANGHAI JIAO TONG UNIV
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Patent Information

Authority / Receiving Office
CN · China
Current Assignee / Owner
SHANGHAI JIAO TONG UNIV
Publication Date
2021-04-30

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Abstract

The invention relates to a reinforcement learning battle game AI training method based on an information bottleneck theory. The method comprises the following steps of 1) initializing an AI training model; 2) performing decision interaction in a simulation environment through the game AI to obtain a sample training batch data set; 3) iteratively training an AI training model by adopting a reinforcement learning algorithm according to a sample training batch data set obtained by interaction between the game AI and the environment, and storing parameters of the AI training model in stages; and (4) fixing part of the stored parameters of the AI training models at different stages, training the remaining parameters again by using a reinforcement learning algorithm to perform fine adjustment to obtain the final AI training models of different levels of AIs, and generating an AI file of the battle game. Compared with the prior art, the method has advantages of high sampling efficiency, high training speed, high test flexibility, AI grading and the like.
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Description

technical field

[0001] The invention relates to the field of game intelligence AI learning, in particular to an AI training method based on information bottleneck theory for reinforcement learning and fighting games. Background technique

[0002] In recent years, with the development of deep learning technology, many achievements have been made in the field of deep reinforcement learning. More and more methods combining deep learning and reinforcement learning algorithms (such as DQN, A2C, PPO, DDPG, etc.) are used in video game AI. However, in many cases, in reinforcement learning problems, the interaction cost between the agent and the environment is very high, so it is hoped that the algorithm can converge as quickly as possible to save training costs, that is, through the same sampling rate and learn a higher level of intelligent strategy.

[0003] In the existing battle games, the man-machine duel mode is one of the important parts of the game. The existing game AI is ...

Claims

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