q = np.zeros((6, 6)) gmma = 0.8 epsilion = 0.3 a = 0.2 for episode in range(1000): # give a random state of the agent , 0-5 state = np.random.randint(0, 6) if state == 5: print(state, " reach directly") else: print(state, end="") # while the state is not reach...
"""This part of code is the Q learning brain, which is a brain of the agent.All decisions are made in here."""import numpy as npimport pandas as pdclass QLearningTable:def __init__(self, actions, learning_rate=0.01, reward_decay=0.9, e_greedy=0.9):self.actions = actions # a lis...
CurrentDirection = 1; % 当前机器人的朝向(1-4具体指向如下) %reference code link: https://mbd.pub/o/bread/ZJqYm5hu % 1 - means robot facing up % 2 - means robot facing left % 3 - means robot facing right % 4 - means robot facing down %reference code link: https://mbd.pub/o/...
原文链接:https://medium.freecodecamp.org/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe 入门强化学习
实现代码github仓库:RL-BaselineCode 代码库将持续更新,希望得到您的支持⭐,让我们一起进步! 文章目录 1. 原理讲解 1.1 Q值更新公式 1.2 ε-greedy随机方法 2. 算法实现 2.1 算法简要流程 2.2 游戏场景 2.3 算法实现 3. 参考文章 1. 原理讲解 Q-learning算法实际上相当简单,仅仅维护一个Q值表即可,表的维数为...
"""This part of code is the Q learning brain, which is a brain of the agent.All decisions are made in here.构成定义一个agent,可以基于环境决策和学习这个class(agent)包含:1超参数(—__init__()),2选择动作的策略(ε-greedy),3学习策略(Qlearning)View more on my tutorial page: https://mor...
Q-learning是一种离散时间强化学习算法,无需模型即可直接从环境中学习最优策略。当应用于小车弧线轨迹行驶控制时,其核心任务是让小车自主学习如何控制转向和速度,以在指定的曲线上稳定行驶。 2.1强化学习基础 强化学习的基本框架由四部分组成:环境(Environment)、代理(Agent)、状态(State)、动作(Action)和奖励(Reward)...
原文链接:https://medium.freecodecamp.org/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe 本文为机器之心编译,转载请联系本公众号获得授权。 ✄--- 加入机器之心(全职记者/实习生):hr@jiqizhixin.com 投稿或寻求报道:editor...
并且,我将训练一个打「毁灭战士(Doom)」游戏、能杀死敌人的智能体!(敬请期待机器之心的更新～ 原文链接:https://medium.freecodecamp.org/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe 本文为机器之心编译,转载请联系本公众号获得授权。
原文链接:https://medium.freecodecamp.org/diving-deeper-into-reinforcement-learning-with-q-learning-c18d0db58efe 本文为机器之心编译,转载请联系本公众号获得授权。 ✄--- 加入机器之心(全职记者/实习生):hr@jiqizhixin.com 投稿或寻求报道:editor...