This script is the main part which controls the update method of this example. The RL is in RL_brain.py. View more on my tutorial page: https://morvanzhou.github.io/tutorials/ """ from maze_env import Maze from
在这个公式中,\alpha代表学习率(learning rate),\gamma是折扣因子(discount factor),这两个参数的值应当在0到1之间。 r是当前得到的reward,Q_{max} (s_{t+1}, a)指在下一个状态s_{t+1}的所有可能的行动之中,Q-value最高的那个行动所对应的Q-value。 4. 然后重复执行步骤2和3,直到训练完成。 pytho...
简单的一个迷宫例子就是这个走迷宫了~从任意状态开始,走到房间5就算成功了~ python实现Q学习走迷宫: 1#an example for maze using qlearning, two dimension2importnumpy as np34#reward matrix R5R = np.array([[-1, -1, -1, -1, 0, -1], [-1, -1, -1, 0, -1, 100],6[-1, -1, -1...
[Debug Example 3] snake_head_x=80, snake_head_y=80, food_x=200, food_y=200, Ne=40, C=40, gamma=0.7checkpoint3.npyNote that for one part of the autograder, we will run your training process on different settings of parameters andcompare the Q-table generated exactly when snake ...
我们将要解决「forest fire」的马尔科夫决策问题,这个在python的 MDP 工具箱(http://pymdptoolbox.readthedocs.io/en/latest/api/example.html)中是可以看到的。 森林由两种行动来管理:「等待」和「砍伐」。我们每年做出一个行动,首要目标是为野生动物维护一片古老的森林,次要目标是伐木赚钱。每年都会以 p 的概率...
Q-learning application Before applying a Q-learning model, it's critical to first understand the problem and how Q-learning training can be applied to that problem. Set up Q-learning in Python with a standard code editor or anintegrated development environmentto write the code. To apply and ...
Q-learning解决悬崖问题 Q-learning是一个经典的强化学习算法,是一种基于价值(Value-based)的算法,通过维护和更新一个价值表格(Q表格)进行学习和预测。 Q-learning是一种off-policy的策略,也就是说,它的行动策略和Q表格的更新策略是不一样的。 行动时,Q-learning会采用epsilon-greedy的方式尝试多种可能动作。
技术标签:python算法强化学习人工智能 实战内容: 1、一维探宝 2、二维探宝 一、实际效果: 一维探宝: 二维探宝: 二、Q-learning算法: 输入: 环境E:用于对机器人做出的动作进行反馈,反馈当前奖励r(本设计中,规定拿到宝藏才有奖励,落入陷阱获得负奖励,其余无奖励)与下个状态state'。如实际效果中... ...
How Does Q-Learning Work? We will learn in detail how Q-learning works by using the example of a frozen lake. In this environment, the agent must cross the frozen lake from the start to the goal, without falling into the holes. The best strategy is to reach goals by taking the shorte...
Reinforcement Learning Analogy The Reinforcement Learning Process Example Design: Self-Driving Cab 1. Rewards 2. State Space 3. Action Space Implementation with Python Gym's interface Reminder of our problem Back to our illustration The Reward Table Solving the environment without Reinforcement Learning ...