7 Starting A Real Project

本文详细介绍了如何从零开始搭建一个React项目的过程,包括初始化项目、安装依赖包如Babel、Webpack等,并配置相应的开发服务器。

mkdir 7reactforeveryone
cd 7reactforeveryone/

npm init

cat package.json
npm install babel-core@latest –save-dev
npm install babel-loader@latest –save-dev
npm install webpack@latest webpack-dev-server@latest –save-dev
npm install react@latest –save
cat package.json

{
  "name": "7reactforeveryone",
  "version": "1.0.0",
  "description": "",
  "main": "index.js",
  "scripts": {
    "test": "echo \"Error: no test specified\" && exit 1"
  },
  "author": "",
  "license": "ISC",
  "dependencies": {
      "react": "^15.4.2"
  },
  "devDependencies": {
    "babel-core": "^6.22.1",
    "babel-loader": "^6.2.10",
    "webpack": "^1.14.0",
    "webpack-dev-server": "^1.16.2"
  }
}
### UC Berkeley CS 188 Project 2 Guide and Resources For the second project of UC Berkeley's CS 188 course on Artificial Intelligence, students typically engage with reinforcement learning concepts through practical application. The focus is often on implementing algorithms that allow agents to learn optimal behaviors within given environments. The official course materials provide comprehensive guidance including problem sets designed specifically for this educational purpose[^1]. These documents outline objectives such as understanding how value iteration works or applying Q-learning techniques effectively. Additionally, they offer starter code which can be invaluable when starting out; it usually includes necessary libraries like BURLAP - Brown-UMBC Reinforcement Learning and Planning, a library written in Java [^1]. Students are encouraged not only to follow provided instructions closely but also experiment beyond them by tweaking parameters or exploring alternative strategies. This hands-on approach helps deepen comprehension while developing critical thinking skills essential for solving complex problems encountered later during more advanced studies or real-world applications. To get started efficiently: - Review lecture notes covering relevant theories. - Study previous years' solutions (if available). - Participate actively in discussion forums where peers share insights gained from their own experiences tackling similar challenges. ```python # Example Python Code Snippet Related To Value Iteration Algorithm Implementation For Educational Purposes Only def value_iteration(mdp, epsilon=0.001): V = {s: 0 for s in mdp.states} delta = float('inf') while delta >= epsilon * (1-mdp.discount_factor)/mdp.discount_factor: new_V = {} for state in mdp.states: q_values = [] for action in mdp.actions(state): total = sum(prob*(reward + mdp.discount_factor*V[next_state]) for next_state, prob, reward in mdp.transitions(state,action)) q_values.append(total) if q_values: best_action_value = max(q_values) new_V[state] = best_action_value delta = max(abs(new_V[s]-V.get(s,0)) for s in new_V.keys()) V.update(new_V) policy = {} for state in mdp.states: actions_qvalues = [(a,sum(p*(r+mdp.discount_factor*V[n]) for n,p,r in mdp.transitions(state,a))) for a in mdp.actions(state)] if actions_qvalues: best_action,_ = max(actions_qvalues,key=lambda x:x[1]) policy[state]=best_action return policy,V ```
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