Python-Machine Learning-Advanced Practice-NetEase Cloud Classroom
Chapter 1 Overview of Artificial Intelligence
1.1 Concept and history of artificial intelligence
1.2 Development trends and challenges of artificial intelligence
1.3 Ethical and social aspects of artificial intelligence
Chapter II: Foundations of Mathematics
1.1 Linear algebra
1.2 Probability and Statistics
1.3 Calculus
Chapter III: Supervised Study
1.1 Unsupervised learning
1.2 Semi-supervised learning
1.3 Reinforcement learning
Chapter 4: Deep Learning
1.1 Fundamentals of neural networks
1.2 Algorithms and applications of deep learning
Chapter V: Natural Language Processing
1.1 Language model
1.2 Text classification
1.3 Information Retrieval
Chapter 6: Computer Vision
1.1 Image classification
1.2 Object detection
1.3 Image segmentation
Chapter 7: Reinforcement Learning
1.1 Basic concepts of reinforcement learning
1.2 Value function and state value
1.3 Algorithms for reinforcement learning
Chapter VIII: Data Preprocessing and Feature Engineering
1.1 Data cleaning and dataset division
1.2 Feature selection and feature extraction
1.3 Feature conversion and feature standardization
Chapter 9 Model Evaluation and Tuning
1.1 Model evaluation indicators
1.2 Training and testing sets
1.3 Balance of bias and variance
1.4 Hyperparameter tuning and model selection
Chapter X: Practical Projects
1.1 Machine learning practical projects
1.2 Deep learning practical projects
1.3 Natural language processing practical projects
1.4 Computer vision practical projects
Chapter III: Supervised Study
1.1 Supervised learning
1.2 Unsupervised learning
1.3 Semi-supervised learning
1.4 Reinforcement learning
Supervised learning
I. Introduction
One of the areas that has received the most attention in recent years is artificial intelligence machine learning, which is a technology that allows computers to automatically learn and improve performance through computer programs and algorithms. Supervised learning is an important branch of machine learning that lets a computer learn how to predict the label or output of unknown data through the labels or outputs of a given dataset. This article will introduce the concepts, methods, applications, and future trends of supervised learning in detail.
Second, the concept of supervised learning
Supervised learning is a machine learning method that trains a model given a labeled dataset. In supervised learning, we know the input characteristics and corresponding outputs of each data point. The goal of supervised learning is to predict the output of unknown data by learning patterns and patterns in the training dataset. The basic idea of supervised learning is to build a functional model by training the samples in the data set so that for new input data, its output can be predicted.
In supervised learning, the input dataset can be represented as X = {x1, x2, ..., xn}, where xi represents a feature vector that contains all the features of the input data. The output dataset can be represented as Y = {y1, y2, ..., yn}, where yi represents the output value corresponding to the input data xi. The goal of supervised learning is to learn a function f(x) = y such that for an unknown input data x, its output y can be predicted.
Third, the method of supervised learning
The methods of supervised learning mainly include classification and regression. Classification refers to dividing input datasets into different categories, and regression refers to predicting the output values of input datasets. Both methods are described in detail below.
1. Classification
Classification is one of the most commonly used methods in supervised learning, and its goal is to divide input datasets into different categories. In classification, each sample has a label that indicates which category the sample belongs to. The goal of a classification model is to predict new categories of unknown data by learning samples from the training dataset.
Commonly used classification algorithms include decision trees, support vector machines, naïve Bayes, K neighbors, etc. Among them, decision tree is a very intuitive classification method, which represents the process of classification by building a tree, each node represents a feature, and each leaf node represents a category. Support vector machine is a classification method based on statistical learning theory, which constructs a hyperplane that can separate different categories by mapping input data into a high-dimensional feature space. Naive Bayes is a classification method based on Bayes' theorem, which assumes that different features are independent of each other and classifies by calculating the conditional probability of each feature in different categories. The K-nearest neighbor algorithm is a classification method based on distance measures that assumes that the class of K training samples closest to an unknown sample is the class of that unknown sample.
2. Regression
Regression is another commonly used method in supervised learning where the goal is to predict the output values of the input dataset. In regression, each sample has a real output value. The goal of a regression model is to predict the output value of new unknown data by learning samples from the training dataset.
Commonly used regression algorithms include linear regression, polynomial regression, ridge regression, LASSO regression, etc. Among them, linear regression is a linear model-based regression method that assumes a linear relationship between input features and outputs, and fits the model by minimizing the error between the predicted value and the true value. Polynomial regression is a polynomial model-based regression method that maps input features into a high-dimensional space and predicts output values by fitting a polynomial function. Both ridge regression and LASSO regression are regularization-based regression methods that control the complexity of the model by adding regularization terms to prevent overfitting.
4. Application of supervised learning
Supervised learning has a wide range of applications in practical applications, and here are a few typical application areas.
1. Image recognition
Image recognition is an important application area in supervised learning, and its goal is to classify or recognize images. Image recognition can be applied in many fields, such as face recognition, vehicle recognition, object recognition, etc. In image recognition, commonly used algorithms include convolutional neural networks (CNNs), support vector machines (SVMs), etc.
2. Natural language processing
Natural language processing refers to the technology of analyzing and processing natural language text, including text classification, sentiment analysis, machine translation, etc. Supervised learning has a wide range of applications in natural language processing, such as text classification based on naïve Bayes, sentiment analysis based on support vector machines, etc.
3. Financial forecasting
Supervised learning also has a wide range of applications in the financial field, such as stock forecasting and credit evaluation. Supervised learning algorithms can analyze and learn from historical data to predict future stock prices and credit ratings, among other things.
4. Medical diagnosis
Supervised learning also has a wide range of applications in the medical field, such as disease diagnosis and drug discovery. Supervised learning algorithms can analyze and learn from medical data to assist doctors in disease diagnosis and drug discovery.
5. Future development trends of supervised learning
As one of the core technologies of machine learning, supervised learning will have wider applications and more in-depth research in the future. Here are a few trends in the future of supervised learning.
1. Deep learning
Deep learning is a machine learning method based on neural networks that models and learns from complex nonlinear relationships. Deep learning has achieved great success in the fields of image recognition and natural language processing, and will continue to be developed and applied in the future.
2. Multi-task learning
Multi-task learning is a machine learning method that can learn multiple tasks at the same time, which can improve the generalization ability and efficiency of the model. Multi-task learning has a wide range of applications in medical diagnosis, natural language processing and other fields.
3. Weakly supervised learning
Weakly supervised learning is a machine learning method that can learn without complete labeled data, and it can learn models using partially labeled data or weakly labeled data. Weakly supervised learning has a wide range of applications in image recognition, natural language processing and other fields, which can effectively reduce the cost of labeled data.
4. Explainability, machine learning
Explainability Machine learning refers to the ability of machine learning models to provide interpretation and understanding of predicted outcomes. With the popularization of machine learning in practical applications, there is an increasing demand for the interpretability and credibility of models. In the future, interpretable machine learning will become an important research direction.
5. Federated learning
Federated learning is a distributed machine learning method that trains and learns models without sharing data, protecting users' privacy and data security. Federated learning has a wide range of applications in finance, medical and other fields, and will become a popular research direction in the future.
In short, supervised learning, as an important branch of machine learning, will continue to play an important role in the future development and lead the continuous progress of machine learning technology.
Unsupervised learning
First, the concept of unsupervised learning
Unsupervised learning is a machine learning method whose goal is to discover the structure and regularity of the data itself in unlabeled data to extract useful information. Unlike supervised learning, unsupervised learning does not have clear target variables or label information, and needs to automatically learn patterns and structures from data for tasks such as classification, clustering, and dimensionality reduction. The application fields of unsupervised learning are very wide, such as data mining, image processing, natural language processing, bioinformatics, etc.
Second, the method of unsupervised learning
Unsupervised learning mainly includes clustering, dimensionality reduction, probability models and other methods, and the commonly used unsupervised learning methods will be introduced in detail below.
1. Clustering
Clustering is an unsupervised learning method that groups similar data points into a class. Clustering divides a dataset into several different groups, each containing data points with similar characteristics. The goal of the clustering algorithm is to minimize differences within groups while maximizing differences between groups in order to find optimal clustering results.
Commonly used clustering algorithms include K-Means clustering, hierarchical clustering, DBSCAN clustering, etc.
K-Means clustering is a distance-based clustering algorithm that iteratively calculates the distance of each data point to the cluster center, dividing the data points into clusters where the nearest cluster center is located. The advantage of K-Means clustering is high computational efficiency, and the disadvantage is that the number of clusters needs to be specified in advance.
Hierarchical clustering is a tree-based clustering algorithm that treats a dataset as a tree structure to divide data points into different clusters. Hierarchical clustering can be divided into two methods: bottom-up aggregation clustering and top-down split clustering.
DBSCAN clustering is a density-based clustering algorithm that divides data points into high-density areas while treating low-density areas as noise points. The advantage of DBSCAN clustering is that the number of clusters can be determined automatically, and the disadvantage is that it is not effective for datasets with uneven density.
2. Dimensionality reduction
Dimensionality reduction is an unsupervised learning method that maps high-dimensional data to low-dimensional spaces. Dimensionality reduction can reduce the dimensionality of data and improve data visualization and processing efficiency. The dimensionality reduction algorithm can be divided into two methods: linear dimensionality reduction and nonlinear dimensionality reduction.
Commonly used linear dimensionality reduction algorithms include principal component analysis (PCA) and factor analysis (FA).
PCA is a linear dimensionality reduction algorithm based on eigenvalue decomposition, which maps data into a new low-dimensional space by calculating the eigenvalues and eigenvectors of the data covariance matrix. The advantage of PCA is high computational efficiency, and the disadvantage is that it cannot handle nonlinear transformation data.
FA is a linear dimensionality reduction algorithm based on factor models that maps data into new low-dimensional spaces by analyzing factor models. The advantage of FA is that it can handle nonlinear transformation data, and the disadvantage is high computational complexity.
Commonly used nonlinear dimensionality reduction algorithms include manifold learning and autoencoders.
Manifold learning is a nonlinear dimensionality reduction algorithm based on manifold structure, which maps high-dimensional data into low-dimensional space by preserving the manifold structure of the data in low-dimensional space. Commonly used manifold learning methods include local linear embedding (LLE), isometric mapping (Isomap), etc.
Autoencoder is a neural network-based nonlinear dimensionality reduction algorithm that maps high-dimensional data into low-dimensional space by training encoders and decoders. The advantage of autoencoder is that it can handle nonlinear transformation data, and the disadvantage is that it requires a large amount of training data.
3. Probabilistic models
Probabilistic modeling is an unsupervised learning method that describes the distribution of data by building a probabilistic model. Probabilistic models can perform tasks such as probabilistic inference, model generation, and so on.
Commonly used probabilistic models include Gaussian mixture models (GMM), hidden Markov models (HMM), variational autoencoders (VAEs), etc.
GMM is a probabilistic model based on Gaussian distributions that describes the distribution of data by decomposing it into a hybrid model of multiple Gaussian distributions. The advantage of GMM is that it can handle multimodal data, and the disadvantage is that the number of hybrid models needs to be specified in advance.
HMM is a hidden state-based probabilistic model that decomposes data into observation sequences and hidden state sequences to describe the temporal relationships of data. HMM has the advantage that it can process time series data, but the disadvantage is that it needs to specify the number of hidden states in advance.
VAE is a neural network-based probabilistic model that learns the distribution of data by training encoders and decoders, and generates new data. The advantage of VAE is that it can process nonlinear transformation data while generating new data, but the disadvantage is that it requires a large amount of training data.
Third, the application of unsupervised learning
Unsupervised learning has a wide range of applications, and common unsupervised learning applications are described below.
1. Image processing
Unsupervised learning has a wide range of applications in the field of image processing, such as image segmentation, image noise reduction, image watermarking and other tasks. Among them, the clustering algorithm can be used for image segmentation, the dimensionality reduction algorithm can be used for image compression and noise reduction, and the probabilistic model can be used for image watermark removal.
2. Natural language processing
Unsupervised learning also has a wide range of applications in the field of natural language processing, such as text classification, language models, machine translation and other tasks. Among them, clustering algorithms can be used for text clustering and topic models, dimensionality reduction algorithms can be used for text classification and language models, and probabilistic models can be used for machine translation and text generation.
3. Data mining
Unsupervised learning also has a wide range of applications in the field of data mining, such as anomaly detection, recommendation systems, market analysis and other tasks. Among them, clustering algorithms can be used for anomaly detection and market analysis, dimensionality reduction algorithms can be used for data visualization and recommendation systems, and probabilistic models can be used for user behavior modeling and prediction.
Fourth, the challenges of unsupervised learning
Unsupervised learning presents many challenges, a few of which are described below.
1. Data quality
The performance of unsupervised learning is highly dependent on the quality of the data, so data preprocessing and cleaning are important.
2. Data dimensions
The processing of high-dimensional data is an important problem in unsupervised learning, because high-dimensional data leads to increased complexity of algorithms and is difficult to visualize and interpret.
3. Model selection
There are many different algorithms and models in unsupervised learning, and how to choose the right algorithm and model is a challenge.
4. Evaluation indicators
Since unsupervised learning is performed without labels, how to evaluate the performance of algorithms is also a challenge. Commonly used evaluation indicators include distance within clusters, distance between clusters, variance after dimensionality reduction, etc.
5. Explainability
Models in unsupervised learning are generally more difficult to interpret than models in supervised learning because they do not have explicit labels and objective functions. Therefore, how to improve the interpretability of the model is a challenge.
In conclusion, unsupervised learning is a very important research field with a wide range of application prospects. While it faces many challenges, as algorithms and models continue to evolve, we believe that unsupervised learning will play an important role in many areas.
Semi-supervised learning
Overview of semi-supervised learning
Semi-supervised learning is a learning method between supervised learning and unsupervised learning, which aims to use a small amount of labeled data and a large amount of unlabeled data for model training, so as to improve the generalization ability and effect of the model. Compared with supervised learning, semi-supervised learning can use more data for training, so as to better solve the problems of data scarcity and labeling difficulty. Compared with unsupervised learning, semi-supervised learning can be supervised with a small amount of labeled data, thereby improving the accuracy and interpretability of the model.
The application fields of semi-supervised learning are very wide, such as text classification, image classification, object recognition, recommendation systems, etc. In these applications, labeled data is often difficult to obtain or expensive, so semi-supervised learning can greatly improve the effectiveness and efficiency of the model.
Second, semi-supervised learning algorithms
Semi-supervised learning algorithms can be divided into two categories: generative model-based methods and discriminant model-based methods. Some common semi-supervised learning algorithms are described below.
1. A model-based approach
Semi-supervised learning methods based on generative models typically use unlabeled data to learn the distribution of data, and then use this distribution to infer the distribution of labeled data. Common methods include naïve Bayes semi-supervised learning, semi-supervised Gaussian mixture models, etc.
Naive Bayes semi-supervised learning is a semi-supervised learning method based on the Naive Bayes classifier. It assumes that each feature is independent, then uses unlabeled data to learn the distribution of each feature, and finally uses this distribution to infer the distribution of labeled data. Due to the simplicity and efficiency of naïve Bayes classifiers, this method has been widely used in fields such as text classification.
A semi-supervised Gaussian mixture model is a semi-supervised learning method based on a Gaussian mixture model. It assumes that the distribution of the data is a mixture of multiple Gaussian distributions and uses unlabeled data to learn the parameters of these distributions. This model is then used to infer categories of labeled data. Since Gaussian mixture models can learn complex data distributions, this method is widely used in areas such as image classification.
2. Method based on discriminant model
Semi-supervised learning methods based on discriminant models usually use unlabeled data and labeled data to learn a discriminant function together, and then use this function to predict the categories of unlabeled data. Common methods include semi-supervised support vector machines, semi-supervised K neighbors, etc.
A semi-supervised support vector machine is a semi-supervised learning method based on a support vector machine. It uses unlabeled data to learn the distribution of the data, and then uses this distribution to infer categories of labeled data. Unlike traditional support vector machines, it also considers the impact of unlabeled data and balances the relationship between labeled and unlabeled data with multiple optimization goals.
Semi-supervised k-nearest neighbor is a semi-supervised learning method based on k-nearest neighbor algorithm. It uses labeled data and unlabeled data to learn a distance measurement function, and then uses this function to calculate the similarity between unlabeled data and labeled data, and takes the category of labeled data with the highest similarity as the predicted category of unlabeled data. Since the k-nearest neighbor algorithm is very simple and intuitive, this method is also very common in practical applications.
3. Other methods
In addition to generative model-based and discriminant-model-based methods, there are some other semi-supervised learning methods, such as collaborative semi-supervised learning, graph semi-supervised learning, etc.
Collaborative semi-supervised learning is a method that uses multiple models to collaborate for semi-supervised learning. It improves the performance and generalization of models by integrating different models. Since multiple models can work together, this approach is also very effective in practical applications.
Graph semi-supervised learning is a method of semi-supervised learning using graph structures. It treats the data as nodes in the graph, the relationships between the data as edges in the graph, and then uses this graph structure for semi-supervised learning. Since graphs can reflect complex relationships between data, this method has been widely used in social network analysis, recommendation systems and other fields.
Third, the advantages and disadvantages of semi-supervised learning
Semi-supervised learning has the following advantages:
1. More data can be used for training, so as to improve the generalization ability and effect of the model.
2. A small amount of labeled data can be used for supervision to improve the accuracy and interpretability of the model.
3. It can solve the problems of data scarcity and labeling difficulty, so that it can be applied to many practical application scenarios.
But semi-supervised learning also has some disadvantages:
1. It is difficult to process unlabeled data, and it is easy to overfit and underfit.
2. Assumptions need to be made about the distribution of unlabeled data and are susceptible to assumptions.
3. In practical applications, how to choose the appropriate semi-supervised learning algorithm and parameter settings is very critical.
4. Application of semi-supervised learning
Semi-supervised learning has been widely used in many practical application scenarios, such as text classification, image classification, object recognition, recommendation systems, etc. Some common application scenarios are described below.
1. Text classification
In text classification, semi-supervised learning can leverage large amounts of unlabeled text data to improve the accuracy and generalization ability of the model. Common methods include naïve Bayes semi-supervised learning, semi-supervised support vector machines, etc.
2. Image classification
In image classification, semi-supervised learning can utilize a large amount of unlabeled image data to improve the accuracy and generalization ability of the model. Common methods include semi-supervised Gaussian mixture models, semi-supervised k-nearest neighbors, etc.
3. Object recognition
In object recognition, semi-supervised learning can utilize a large amount of unlabeled image data to improve the accuracy and generalization ability of the model. Common methods include semi-supervised support vector machines, collaborative semi-supervised learning, etc.
4. Referral system
In recommender systems, semi-supervised learning can leverage large amounts of unlabeled user data to improve the accuracy and generalization ability of the model. Common methods include collaborative filtering, matrix factorization, etc. Semi-supervised learning can use the user's historical behavior data, such as purchase history, browsing history, etc., to recommend items, thereby improving the accuracy and personalization of recommendations.
5. Development trend of semi-supervised learning
With the increasing amount of data and the inadequacy of labeled data, the value of semi-supervised learning in practical applications is becoming more and more important. The development trend of semi-supervised learning in the future mainly includes the following aspects:
1. More efficient algorithms and models
Future semi-supervised learning will require more efficient algorithms and models to process large-scale data, and the scalability and interpretability of the models need to be considered.
2. More accurate predictions and recommendations
In the future, semi-supervised learning requires more accurate prediction and recommendation methods to meet the needs of practical applications, and needs to consider data sparsity and labeling difficulty in different scenarios.
3. A more flexible framework for semi-supervised learning
In the future, semi-supervised learning needs a more flexible semi-supervised learning framework to adapt to different application scenarios, and the characteristics and differences of different data types need to be considered.
4. A more open platform for data sharing and annotation
The future of semi-supervised learning will require more open data sharing and labeling platforms so that more researchers and engineers can take advantage of large amounts of unlabeled data for training and testing.
In general, semi-supervised learning will play an increasingly important role in the development of the future and will be widely used in many practical application scenarios.
Enhance learning
I. Introduction
Reinforcement Learning (RL) is an important branch of machine learning that studies how to maximize the cumulative reward of an agent in its interaction with the environment through exploration and learning. Unlike supervised and unsupervised learning, the goal of reinforcement learning is to enable the agent to learn optimal behavioral strategies in the environment, rather than learning the mapping relationship between input and output.
The applications of reinforcement learning are very wide, including robot control, game AI, autonomous driving, financial trading and other fields. This article will introduce the basic principles, algorithms, and applications of reinforcement learning in detail.
Second, the basic principles of enhanced learning
Reinforcement learning is a trial-and-error-based approach to learning optimal behavioral strategies through the interaction of agents with their environment. In reinforcement learning, the agent decides on the next action by observing the state of the environment and the reward signal, and updates its own strategy based on the outcome of the action.
1. Strengthen the signal
In reinforcement learning, the agent acquires a Reward Signal by interacting with the environment, which is used to evaluate whether it is behaving correctly. A reinforcement signal is a scalar that represents the reward or punishment received by an agent for taking an action in a specific state. The purpose of the reinforcement signal is to allow the agent to obtain feedback from the environment in order to better adjust its behavior strategy.
2. State space and action space
In reinforcement learning, the agent's interaction with the environment can be formalized as a Markov Decision Process (MDP). The MDP consists of the quintuple $(S,A,P,R,\gamma)$, where:
- $S$ represents the state space, including all possible states.
- $A$ indicates space for action, including all possible actions.
- $P$ indicates the state transition probability, that is, the probability of moving to the next state after taking an action in the current state.
- $R$ indicates a reinforcement signal, i.e. the reward or punishment for taking an action in the current state.
- $\gamma$ represents the discount factor, which is used to measure the value of future rewards.
According to the MDP model, the agent can choose the next action by observing the current state and update its own strategy based on the obtained reward signal.
3. Strategy and value function
In reinforcement learning, a policy is the probability distribution of an agent taking an action in a particular state. Policies can be deterministic or random.
The value function refers to the expected cumulative reward for taking an action in a specific state. Value functions can be divided into two types:
- State Value Function, which represents the expected cumulative reward for taking an action in the current state.
- Action Value Function, which represents the expected cumulative reward that can be obtained after taking an action in the current state.
4. Strategy evaluation and strategy improvement
In reinforcement learning, agents need to constantly evaluate and improve their strategies in order to obtain higher cumulative rewards.
Policy evaluation refers to the given strategy and calculating the expected cumulative reward that the agent can obtain under the strategy. Strategy evaluation can be achieved by solving the Bellman equation, where the state-value function and the action-value function are defined as follows:
- Status value function: $V(s)=\mathbb{E}[G_t|S_t=s]$
- Action value function: $Q(s,a)=\mathbb{E}[G_t|S_t=s,A_t=a]$
where $G_t=\sum_{k=0}^{\infty}\gamma^kR_{t+k+1}$ represents the cumulative sum of future rewards starting from the moment $t$.
Policy improvement refers to updating the agent's strategy to obtain a higher cumulative reward based on the results of the strategy evaluation. Strategy improvement can be achieved by solving greedy strategies, that is, selecting actions in each state that will receive the greatest expected reward.
5. Policy iteration and value iteration
Policy iteration refers to continuous strategy evaluation and policy improvement until the strategy converges. Policy iteration consists of the following steps:
- Initialize the policy $\pi_0$
- Evaluate the strategy and calculate the value function $V_{\pi_k}$ of the current strategy
- Make policy improvements, update policy $\pi_{k+1}$
- Check if the strategy converges, if it converges, output the optimal strategy, otherwise return to the second step
Value iteration refers to the continuous iterative update of the value function until the value function converges. Value iteration consists of the following steps:
- Initialize the value function $V_0$
- Iterative update of the value function until convergence: $V_{k+1}(s)=\max_a\sum_{s',r}p(s',r|s,a)[r+\gamma V_k(s')]$
- 输出最优策略$\pi^*(s)=\arg\max_a\sum_{s',r}p(s',r|s,a)[r+\gamma V^*(s')]$
Third, enhance the learning algorithm
Reinforcement learning algorithms can be divided into value-function-based and policy-based approaches. Value function-based algorithms mainly include Q-learning, SARSA and DQN, and policy-based algorithms mainly include REINFORCE, Actor-Critic and PPO.
1. Q-learning
Q-learning is an enhanced learning algorithm based on the value function, which selects the optimal action by learning the action value function $Q(s,a)$. The update rules for Q-learning are as follows:
$Q(s_t,a_t)\leftarrow Q(s_t,a_t)+\alpha[r_{t+1}+\gamma\max_aQ(s_{t+1},a)-Q(s_t,a_t)]$
where $\alpha$ is the learning rate, and $r_{t+1}$ is the reward signal at the current moment. The core idea of Q-learning is to update the action value function based on the current state and action, so that the agent can learn the optimal action strategy.
2. SAUCE
SARSA is a value function-based reinforcement learning algorithm that selects the next action by learning the action value function $Q(s,a)$. The update rules for SARSA are as follows:
$Q(s_t,a_t)\leftarrow Q(s_t,a_t)+\alpha[r_{t+1}+\gamma Q(s_{t+1}, a_{t+1})-Q(s_t,a_t)]$
where $\alpha$ is the learning rate, $r_{t+1}$ is the reward signal at the current moment, and $a_{t+1}$ is the next action.
Unlike Q-learning, SARSA takes into account the next action $a_{t+1}$ when updating the action value function, while Q-learning selects the maximum action value function in the current state to update. As a result, SARSA is more stable, but may lead to convergence to suboptimal strategies.
3. DQN
DQN is a value-function-based reinforcement learning algorithm that uses a deep neural network to approximate the action value function $Q(s,a)$. The update rules for DQN are as follows:
$y_t=r_t+\gamma\max_{a'}Q(s_{t+1},a';\theta^-)$
$Loss=(y_t-Q(s_t,a_t;\theta))^2$
$\theta\leftarrow\theta-\alpha\nabla_{\theta}Loss$
where $r_t$ is the reward signal at the current moment, $\theta$ is the network parameter, $\theta^-$ is the parameter of the target network, and $\alpha$ is the learning rate.
DQN improves learning efficiency and stability by using Experience Replay and Target Network.
4. REINFORCE
REINFORCE is a policy-based reinforcement learning algorithm that obtains the optimal action strategy by directly optimizing the strategy. THE UPDATE RULES FOR REINFORCE ARE AS FOLLOWS:
$\theta\leftarrow\theta+\alpha\nabla_{\theta}\log\pi_{\theta}(a_t|s_t)G_t$
where $\theta$ is the policy parameter, $\alpha$ is the learning rate, and $G_t$ is the sum of future rewards starting from the moment $t$.
REINFORCE USES THE MONTECARLO METHOD TO ESTIMATE THE EXPECTED CUMULATIVE REWARD AND UPDATES THE POLICY PARAMETERS WITH THE STRATEGY GRADIENT DESCENT. ALTHOUGH REINFORCE TENDS TO FALL INTO LOCAL OPTIMUM, IT CAN HANDLE DISCRETE AND CONTINUOUS ACTION SPACES AND HAS GOOD CONVERGENCE PERFORMANCE.
5. Actor-Critic
Actor-Critic is an enhanced learning algorithm that combines strategy and value functions, using a network of actors to output policies and a network of critics to estimate state value functions or action value functions. The update rules for Actor-Critic are as follows:
$\delta_t=r_{t+1}+\gamma V(s_{t+1})-V(s_t)$
$\theta\leftarrow\theta+\alpha\nabla_{\theta}\log\pi_{\theta}(a_t|s_t)\delta_t$
$V(s_t)\leftarrow V(s_t)+\beta\delta_t$
where $\theta$ is the actor network parameter, $V$ is the value function output by the critic network, and $\alpha$ and $\beta$ are the learning rate.
The Actor-Critic algorithm combines the advantages of policy gradient and value function approximation methods, can handle discrete and continuous action spaces, and has good convergence performance.
6. PPO
PPO is a policy-based reinforcement learning algorithm that improves stability by limiting the magnitude of policy updates. The update rules for PPO are as follows:
$L^{CLIP}(\theta)=\min(r_t(\theta)\hat{A}_t,clip(r_t(\theta),1-\epsilon,1+\epsilon)\hat{A}_t)$
$\theta\leftarrow\arg\max_{\theta}L^{CLIP}(\theta)$
where, $r_t(\theta)=\frac{\pi_{\theta}(a_t|s_t)}{\pi_{\theta_{old}}(a_t|s_t)}$ is the policy ratio, and $\hat{A}_t$ is the advantage function estimate.
PPO improves stability by limiting the magnitude of policy updates to avoid over-updating scenarios. Specifically, PPO uses a shear function to limit the value range of the strategy ratio $r_t(\theta)$, so as to ensure that the update amplitude is not too large.
PPO also uses an advantage function to estimate $\hat{A}_t$ to measure the improvement effect of the current strategy over previous strategies. The dominance function estimate can be calculated by the value function or by the Monte Carlo method.
PPO is an efficient and stable reinforcement learning algorithm that can handle continuous action spaces and achieves excellent performance on many tasks.
In general, the choice of reinforcement learning algorithm depends on the specific problem and application scenario. It is necessary to select the appropriate algorithm according to the characteristics and data volume of the task, and optimize and adjust parameters to obtain the best results.