强化学习入门 (Introduction to Reinforcement Learning)

强化学习基础 (Reinforcement Learning Basics)

强化学习是一种通过与环境交互来学习最优策略的机器学习方法，通过试错和奖励机制来实现目标。

Reinforcement Learning is a machine learning method that learns optimal policies through interaction with an environment, achieving goals through trial-and-error and reward mechanisms.

核心概念 (Core Concepts)

基本要素 (Basic Elements)

状态空间 (State Space)
动作空间 (Action Space)
奖励函数 (Reward Function)
策略函数 (Policy Function)

价值函数 (Value Functions)

状态价值函数 (State Value Function)
动作价值函数 (Action Value Function)
优势函数 (Advantage Function)

探索与利用 (Exploration vs Exploitation)

ε-贪心策略 (ε-greedy Policy)
玻尔兹曼探索 (Boltzmann Exploration)
参数噪声 (Parameter Noise)

经典算法 (Classic Algorithms)

基于价值的方法 (Value-based Methods)

Q-Learning
DQN (Deep Q-Network)
Double DQN

基于策略的方法 (Policy-based Methods)

REINFORCE
Actor-Critic
PPO (Proximal Policy Optimization)

实践应用 (Practical Applications)

游戏AI (Game AI)

import gym
import numpy as np

# 创建环境 (Create environment)
env = gym.make('CartPole-v1')

# Q-learning实现 (Q-learning implementation)
class QLearningAgent:
    def __init__(self, state_size, action_size):
        self.q_table = np.zeros((state_size, action_size))
        self.learning_rate = 0.1
        self.gamma = 0.95
        self.epsilon = 0.1
    
    def choose_action(self, state):
        if np.random.random() < self.epsilon:
            return env.action_space.sample()
        return np.argmax(self.q_table[state])
    
    def learn(self, state, action, reward, next_state):
        old_value = self.q_table[state, action]
        next_max = np.max(self.q_table[next_state])
        new_value = (1 - self.learning_rate) * old_value + \
                   self.learning_rate * (reward + self.gamma * next_max)
        self.q_table[state, action] = new_value

机器人控制 (Robot Control)

运动规划 (Motion Planning)
任务学习 (Task Learning)
多智能体系统 (Multi-agent Systems)

高级主题 (Advanced Topics)

分层强化学习 (Hierarchical RL)

选项框架 (Options Framework)
目标分解 (Goal Decomposition)
技能迁移 (Skill Transfer)

多任务学习 (Multi-task Learning)

任务表示 (Task Representation)
知识迁移 (Knowledge Transfer)
课程学习 (Curriculum Learning)

实战项目 (Hands-on Project)

在下一节中，我们将实现一个强化学习智能体来玩简单的游戏，理解基本的强化学习概念和实现方法。

In the next section, we will implement a reinforcement learning agent to play simple games, understanding basic RL concepts and implementation methods.