WebApr 13, 2024 · 以下内容来源于一次部门内部的分享,主要针对AI初学者,介绍包括CNN、Deep Q Network以及TensorFlow平台等内容。由于笔者并非深度学习算法研究者,因此以下更多从应用的角度对整个系统进行介绍,而不会进行详细的公式推导。* 关于Flappy Bird * Flappy Bird(非官方译名:笨鸟先飞)是一款2013年鸟飞类游戏 ... WebMay 20, 2024 · Q-learning is a model-free reinforcement learning algorithm which is generally used to learn the best action for an agent to take given a particular state. When …
Using Deep Q-Network to Learn How To Play Flappy Bird
WebMar 29, 2024 · DQN(Deep Q-learning)入门教程(四)之 Q-learning Play Flappy Bird. 在上一篇 博客 中,我们详细的对 Q-learning 的算法流程进行了介绍。. 同时我们使用了贪婪法贪婪法防止陷入局部最优。. 那么我们可以想一下,最后我们得到的结果是什么样的呢?. 因为我们考虑到了 ... WebApr 4, 2024 · As a simpler version of the game, we use the text flappy bird environment and train Q-Learning and SARSA agents. The algorithms Q-learning and SARSA are … greater south haven area community foundation
DQN(Deep Q-learning)入门教程(结束)之总结 -文章频道
WebRL Flappy Bird Overview This project is a basic application of Reinforcement Learning. It integrates Deep Java Library (DJL) to uses DQN to train agent. The pretrained model are trained with 3M steps on a single GPU. You can find article explaining the training process on towards data science, or 中文版文章. Build the project and run WebFlappy Bird - Q Learning: Shooter (custom game): Note: Number of epochs and train cycles has been adjusted such that all the above code when used for traning takes only about 12-15 hrs max. depending on your CPU and GPU (My CPU: i5 3.4 GHz and GPU: nVidia GeForce 660). WebMar 21, 2024 · FlapAI Bird: Training an Agent to Play Flappy Bird Using Reinforcement Learning Techniques Tai Vu, Leon Tran Reinforcement learning is one of the most popular approaches for automated game playing. This method allows an agent to estimate the expected utility of its state in order to make optimal actions in an unknown environment. greater south fulton chamber of commerce