Research And Realization Of Game Strategy Based On Deep Reinforcement Learning

Deep Reinforcement learning is a brand new algorithm that combines deep learning and reinforcement learning to achieve end-to-end learning from perception to action.Simply put,just like human beings,inputting perceptual information such as vision,and then outputting the action directly through deep neural networks without any hand-crafted work in between.Deep reinforcement learning has the potential to enable an agent to learn one or even more of a variety of skills completely autonomously.The deep reinforcement learning is a branch of the rapid development of the field of deep learning in the past two years.The goal is to solve the problem of computer control from perception to decision-making,so as to realize common artificial intelligence.With Google Deep Mind Team as its leader,deep-learning-based algorithms have made breakthroughs in computer vision,speech recognition,natural language processing and other fields,and the related technologies have matured and entered into our lives.However,the issues studied in these areas are simply to enable computers to perceive and understand the environment.At the same time,decision-making control is the core issue to be solved in the field of artificial intelligence.Perception problems such as computer vision require input of perceptual information to the computer,the computer can understand,and the decision-making control problem requires the computer to judge and output according to the perceptual information and output the correct behavior.To enable computers to make good decision-making and control requires that the computer have some "thinking" ability to enable the computer to master the ability to solve various problems through learning,and this is exactly the goal of universal artificial intelligence research.Agent behavior can be attributed to the interaction with the environment.The agent observes the environment,and then outputs the action based on the observation and its own state.As a result,the environment changes and the feedback is returned to the agent.Therefore,the core issue is how to construct such an agent that can interact with the environment.Deep Reinforcement Learning combines deep learning with reinforcement learning.Deep learning is used to provide a learning mechanism,reinforcing learning provides the learning goals for deep learning.This makes Deep Reinforcement Learning possess the ability to construct complex intelligence.In this paper,we use the first deep reinforcement learning algorithm DQN to conduct experiments,put the agent in the game of gluttonous snake environment for training,and the agent will not get any information except image pixels and scores during training,agents must learn by themselves and use inputs and scores directly to develop the best strategy of action.After that,we joined the curriculum learning strategy,combining the idea of course learning with DQN algorithm,and again training the agent on the game,comparing the experimental results.Experiments show that the agent using DQN algorithm can get higher scores and longer survival time after training with the opponent,and the use of the course learning strategy can greatly improve the training speed of the agent and further enhance the performance of the agent.