One article to understand reinforcement learning: RL comprehensive analysis and Pytorch actual combat

In this article, we comprehensively and in-depth discussed the basic concepts, mainstream algorithms and practical steps of Reinforcement Learning. From Markov decision process (MDP) to advanced algorithms such as PPO, the article aims to provide readers with a comprehensive set of theoretical framework and practical tools. At the same time, we also specifically discussed the specific application scenarios of reinforcement learning in multiple fields, such as games, finance, medical care and autonomous driving. Each section provides detailed Python and PyTorch code examples to help better understand and apply the concepts.

Follow TechLead and share all-dimensional knowledge of AI. The author has more than 10 years of experience in Internet service architecture, AI product development, and team management. He is a Fudan master of Tongji, a member of Fudan Robot Intelligence Laboratory, a senior architect certified by Alibaba Cloud, a project management professional, and has hundreds of millions of revenue in AI product development. principal.

I. Introduction

Reinforcement Learning (RL) is an important subfield of artificial intelligence (AI) and machine learning (ML), alongside supervised and unsupervised learning. It mimics the process by which organisms learn optimal behavior by interacting with their environment. Unlike traditional supervised learning, reinforcement learning does not have a pre-labeled dataset to train the model. Instead, it relies on an agent (Agent) to learn how to achieve a specific goal in a given environment through repeated trial, failure, adaptation, and optimization.

Core Components of Reinforcement Learning

The framework of reinforcement learning mainly consists of the following core components:

• State (State) : reflects the current situation of the environment or system.

• Action : Actions that an agent can take in a particular state.

• Reward : A numerical feedback that quantifies the response of the environment after the agent takes a certain action.

• Policy : A mapping function that instructs the agent which action to take in a particular state.

These four elements together constitute the Markov Decision Process (MDP) , which is the core mathematical model of reinforcement learning.

Why is reinforcement learning important?

Practicality and Wide Application

The importance of reinforcement learning is first reflected in its wide application value. From autonomous driving, game AI, to quantitative trading, industrial automation, and breakthroughs in natural language processing and recommendation systems in recent years, reinforcement learning has played an indispensable role.

Adaptation and Optimization

Traditional algorithms are often static, i.e. they do not have the ability to adapt to changing environments or parameters. Reinforcement learning algorithms, on the other hand, can continuously adapt and optimize, which allows them to excel in more complex and dynamic environments.

Pushing the Frontiers of AI Research

Reinforcement learning is also pushing the research frontiers of artificial intelligence, especially in solving some complex problems that require long-term planning and decision-making. For example, reinforcement learning has been successfully applied to the Go algorithm AlphaGo, defeating the human world champion, which marks a major breakthrough in AI's ability to perform complex tasks.

Leading Ethical and Social Thinking

With the increasing application of reinforcement learning in automatic decision-making systems, how to design fair, transparent and explainable algorithms has also raised many ethical and social issues, which requires us to explore and understand all aspects of reinforcement learning more deeply .

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2. Reinforce the basics of learning

The core of reinforcement learning is to model decision-making problems and learn the best decision-making scheme through interaction with the environment. This process is often described and solved through the Markov Decision Process (MDP). In this section, we explore in detail Markov decision processes and their core components: rewards, states, actions, and policies.

Markov Decision Process (MDP)

MDP is a mathematical model used to describe decision-making problems, mainly composed of a quadruple ( (S, A, R, P) ).

• State Space (S) : Represents the set of all possible states.

• Action Space (A) : Represents the set of all actions that may be taken in a particular state.

• Reward function (R) : ( R(s, a, s') ) represents the immediate reward obtained when taking an action ( a ) in state ( s ) and transitioning to state ( s' ).

• Transition probability (P) : ( P(s' | s, a) ) represents the probability of taking action ( a ) in state ( s ) and transferring to state ( s' ).

state

In MDP, state is used to describe the current state of the environment or problem. In different applications, status can have many manifestations:

• In board games, states usually represent the positions of individual pieces on the board.
• In autonomous driving, the state may include the vehicle's speed, position, and the state of surrounding objects.

action

Actions are operations that an agent (Agent) can take in a certain state. Actions affect the environment and may cause state transitions.

• In stock market trading, the action is usually "buy", "sell" or "hold".
• In a game like "Super Mario," actions might include things like "jump," "squat," or "move forward."

Reward

A reward is a numerical feedback that evaluates how "good or bad" an action is for an agent. Typically, the agent's goal is to maximize the cumulative reward.

• In a maze problem, there might be a positive reward for reaching the destination, and a negative reward for hitting a wall.

Policy

A policy is a mapping function from state to action, which is used to guide the agent which action should be taken in each state. Formally, a policy is usually denoted as ( \pi(a|s) ), representing the probability of taking an action ( a ) in a state ( s ).

• In a game like backgammon, the strategy might be a complex neural network that evaluates the merits of each move.

By optimizing the policy, we can make the agent obtain higher cumulative rewards in the interaction with the environment, thus achieving better performance.

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3. Common reinforcement learning algorithms

Reinforcement learning has a variety of algorithms for solving different types of problems. In this section, we will discuss several commonly used reinforcement learning algorithms, including their working principles, significance and application examples.

Value Iteration

Algorithm Description

Value iteration is a Dynamic Programming-based method for computing optimal policies. The main idea is to find the optimal strategy by iteratively updating the state value function (Value Function).

algorithm meaning

Value iteration algorithms are mainly used to solve MDP problems with fully observable states and known transition probabilities. It is a "model known" algorithm.

Applications

Value iteration is often used in environments such as path planning, games such as maze problems, where all states and transition probabilities are known.

Q-Learning

Algorithm Description

Q-learning is a "model-ignorant" algorithm based on a value function. It finds the optimal policy by updating the Q value (state-action value function).

algorithm meaning

The Q-learning algorithm is suitable for "model ignorant" scenarios, that is, the agent does not need to know the complete information of the environment. Therefore, Q-learning is particularly applicable to real-world problems.

Applications

Q-learning is widely used in robot navigation, e-commerce recommendation system and multi-player games, etc.

Policy Gradients (policy gradient)

Algorithm Description

Unlike value function-based methods, policy gradient methods directly optimize in the policy space. The algorithm updates policy parameters by computing gradients.

algorithm meaning

Policy gradient methods are particularly suitable for dealing with high-dimensional or continuous action and state spaces, which are usually difficult to handle in value-based methods.

Applications

Policy gradient methods are widely used in natural language processing (such as machine translation), continuous control problems (such as robot arm control), etc.

Actor-Critic (actor-critic)

Algorithm Description

Actor-Critic combines the advantages of value function methods and policy gradient methods. Among them, "Actor" is responsible for making decisions, and "Critic" is responsible for evaluating these decisions.

algorithm meaning

By combining value function and policy optimization, Actor-Critic achieves faster and more stable learning in a variety of environments.

Applications

Actor-Critic methods are widely used in complex problems such as autonomous driving, resource allocation, and multi-agent systems.

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4. PPO (Proximal Policy Optimization) Algorithm

PPO is an efficient and reliable reinforcement learning algorithm, which is part of the policy gradient family. Due to its efficient and stable nature, the PPO algorithm has been widely used in various reinforcement learning tasks.

Relationship to Reinforcement Learning

PPO is an algorithm for solving Markov decision process (MDP) problems. It allows the agent to choose the optimal action in different states by optimizing the policy (Policy), thereby maximizing the expected cumulative reward.

principle

The core idea of ​​PPO is to avoid too much performance degradation by limiting the step size of policy updates. This is achieved by introducing a special objective function that includes a clipping term to limit how much the policy changes.

The specific objective function is as follows:

detail

• Multi-step advantage estimation : PPO is usually used in combination with multi-step return (Multi-Step Return) and advantage function (Advantage Function) to reduce estimation error.

• Adaptive learning rate : PPO usually uses an adaptive learning rate and an advanced optimizer (such as Adam).

• Parallel sampling : Since PPO is a "sample-efficient" algorithm, it is usually used in conjunction with parallel environment sampling to further improve efficiency.

code example

Here is a simple example of implementing PPO using Python and PyTorch:

import torch
import torch.nn as nn
import torch.optim as optim

# 定义策略网络
class PolicyNetwork(nn.Module):
    def __init__(self, state_dim, action_dim):
        super(PolicyNetwork, self).__init__()
        self.fc = nn.Linear(state_dim, 128)
        self.policy_head = nn.Linear(128, action_dim)

    def forward(self, x):
        x = torch.relu(self.fc(x))
        return torch.softmax(self.policy_head(x), dim=-1)

# 初始化
state_dim = 4  # 状态维度
action_dim = 2  # 动作维度
policy_net = PolicyNetwork(state_dim, action_dim)
optimizer = optim.Adam(policy_net.parameters(), lr=1e-3)
epsilon = 0.2

# 采样数据（这里假设有一批样本数据）
states = torch.rand(10, state_dim)
actions = torch.randint(0, action_dim, (10,))
advantages = torch.rand(10)

# 计算旧策略的动作概率
with torch.no_grad():
    old_probs = policy_net(states).gather(1, actions.unsqueeze(-1)).squeeze()

# PPO更新
for i in range(4):  # Typically we run multiple epochs
    action_probs = policy_net(states).gather(1, actions.unsqueeze(-1)).squeeze()
    ratio = action_probs / old_probs
    surr1 = ratio * advantages
    surr2 = torch.clamp(ratio, 1-epsilon, 1+epsilon) * advantages
    loss = -torch.min(surr1, surr2).mean()

    optimizer.zero_grad()
    loss.backward()
    optimizer.step()

print("PPO Update Done!")

This is just a very basic example, and more elements need to be included in practical applications, such as state standardization, network structure optimization, etc.

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5. Intensive learning practice

5.1 Model Creation

In reinforcement learning practice, model creation is the first and crucial step. Typically, this phase includes environment setup, model architecture design, and data preprocessing, among others. The following is an example of using PyTorch to implement a reinforcement learning model. Here we use a simple CartPole environment as a case.

environment settings

First, we need to install the necessary libraries and set up the environment.

pip install gym
pip install torch

Next, we'll import these libraries:

import gym
import torch
import torch.nn as nn
import torch.optim as optim

Create a gym environment

Using OpenAI's Gym library, we can easily create a CartPole environment:

env = gym.make('CartPole-v1')

model architecture

Next, we design a simple neural network as the policy network. The network will receive the state of the environment as input and output the probability of each action.

class PolicyNetwork(nn.Module):
    def __init__(self, input_dim, output_dim):
        super(PolicyNetwork, self).__init__()
        self.fc1 = nn.Linear(input_dim, 64)
        self.fc2 = nn.Linear(64, output_dim)
    
    def forward(self, state):
        x = torch.relu(self.fc1(state))
        action_probs = torch.softmax(self.fc2(x), dim=-1)
        return action_probs

Initialize the model and optimizer

After defining the model architecture, we need to initialize it and choose an optimizer.

input_dim = env.observation_space.shape[0]  # 状态空间维度
output_dim = env.action_space.n  # 动作空间大小

policy_net = PolicyNetwork(input_dim, output_dim)
optimizer = optim.Adam(policy_net.parameters(), lr=1e-2)

5.2 Model Evaluation

Model evaluation typically involves running simulations under a range of test environments and calculating various performance metrics.

Test environment running

The following code shows how to test the trained model in Gym's CartPole environment:

def evaluate_policy(policy_net, env, episodes=10):
    total_rewards = 0
    for i in range(episodes):
        state = env.reset()
        done = False
        episode_reward = 0
        while not done:
            state_tensor = torch.FloatTensor(state).unsqueeze(0)
            with torch.no_grad():
                action_probs = policy_net(state_tensor)
            action = torch.argmax(action_probs).item()
            next_state, reward, done, _ = env.step(action)
            episode_reward += reward
            state = next_state
        total_rewards += episode_reward

    average_reward = total_rewards / episodes
    return average_reward

# 使用上文定义的PolicyNetwork和初始化的env
average_reward = evaluate_policy(policy_net, env)
print(f"Average reward over {
      
      episodes} episodes: {
      
      average_reward}")

Performance

Performance metrics may include average reward, variance, max/min reward, etc. These metrics help us understand the stability and reliability of the model in different situations.

# 在这里，我们已经计算了平均奖励
# 在更复杂的场景中，你可能还需要计算其他指标，如奖励的标准差等。

5.3 Model online

Model online usually includes model saving, loading and deployment in the actual environment.

Model saving and loading

PyTorch provides a very convenient API to save and load models.

# 保存模型
torch.save(policy_net.state_dict(), 'policy_net_model.pth')

# 加载模型
loaded_policy_net = PolicyNetwork(input_dim, output_dim)
loaded_policy_net.load_state_dict(torch.load('policy_net_model.pth'))

Deploy to the actual environment

The specific steps of model deployment depend on the application scenario. In some online systems, it may be necessary to convert the PyTorch model to ONNX or TensorRT format to improve inference speed.

# 示例：将PyTorch模型转为ONNX格式
dummy_input = torch.randn(1, input_dim)
torch.onnx.export(policy_net, dummy_input, "policy_net_model.onnx")

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Summarize

Reinforcement Learning (RL) is one of the most promising and challenging research directions in artificial intelligence. Through this article, we have explored in depth the core concepts of reinforcement learning, including Markov decision processes (Markov Decision Processes, MDP) and its rewards, states, actions, and policies. We also introduced a variety of mainstream reinforcement learning algorithms, such as Q-Learning, DQN, and PPO, each of which has its own unique advantages and application scenarios.

In the practical part of reinforcement learning, we take the CartPole environment as an example to explain the implementation steps of a complete RL project in an all-round way, from model creation to model evaluation and launch. We also provide exhaustive PyTorch code examples and explanations to help readers better understand and apply these concepts.

Reinforcement learning not only occupies an important position in theoretical research, but also has broad application prospects in practical applications, such as autonomous driving, financial transactions, and medical diagnosis. However, reinforcement learning also faces multiple challenges, including but not limited to data sparsity, training instability, and environment simulation. Therefore, mastering the basic knowledge and practical experience of reinforcement learning will provide powerful tools and perspectives to solve these complex problems.

Follow TechLead and share all-dimensional knowledge of AI. The author has more than 10 years of experience in Internet service architecture, AI product development, and team management. He is a Fudan master of Tongji, a member of Fudan Robot Intelligence Laboratory, a senior architect certified by Alibaba Cloud, a project management professional, and has hundreds of millions of revenue in AI product development. principal.