论文标题
使用加固学习
Chrome Dino Run using Reinforcement Learning
论文作者
论文摘要
强化学习是人类已知的最先进的算法之一，它可以在游戏中竞争，比人类表现更好。在本文中，我们研究了最受欢迎的模型免费强化学习算法以及卷积神经网络，以训练代理商玩Chrome Dino Run游戏。我们使用了两种流行的时间差异方法，即深Q学习，并预期SARSA并实施了双DQN模型来训练该代理，并最终将算法相对于时间播放的算法的发作和收敛性进行比较。
Reinforcement Learning is one of the most advanced set of algorithms known to mankind which can compete in games and perform at par or even better than humans. In this paper we study most popular model free reinforcement learning algorithms along with convolutional neural network to train the agent for playing the game of Chrome Dino Run. We have used two of the popular temporal difference approaches namely Deep Q-Learning, and Expected SARSA and also implemented Double DQN model to train the agent and finally compare the scores with respect to the episodes and convergence of algorithms with respect to timesteps.
