foreword
There are three main categories of machine learning: supervised learning, unsupervised learning, and reinforcement learning. In this article, we will introduce the principles, common algorithms and application areas of Reinforcement Learning.
Article directory
1. Principle
Reinforcement Learning (Reinforcement Learning) is an important learning paradigm in machine learning, whose goal is to learn how to make optimal decisions by interacting with the environment. Reinforcement learning is different from supervised learning and unsupervised learning. It learns through trial and error and does not require labeled training data or prior knowledge of the environment.
The core idea of reinforcement learning is to learn how to make optimal decisions by interacting with the environment.
In reinforcement learning, there is an agent (Agent) and an environment (Environment). The agent selects an action (Action) by observing the state of the environment (State), and then the environment gives a reward (Reward) and a new state according to the action of the agent. The agent adjusts its strategy according to the reward to obtain a higher cumulative reward.
The goal of reinforcement learning is to find an optimal policy (Policy), so that the agent can obtain the maximum cumulative reward in the process of interacting with the environment. This process can be modeled with a Markov Decision Process (MDP). MDP consists of five elements: State Space, Action Space, State Transition Probability, Reward Function and Discount Factor.
2. Algorithm
There are many classic algorithms in reinforcement learning. Here are some common algorithms:
1️⃣Q learning
Q-learning is a reinforcement learning algorithm based on value functions. It learns the optimal policy by iteratively updating an action-value function (Q-function). The core idea of Q-learning is to use Bellman Equation (Bellman Equation) to update the value of Q-function so that it can approach the optimal Q-function.
2️⃣SARSA
SARSA is a reinforcement learning algorithm based on value functions and policies. It is similar to Q-learning, but uses the next action value under the current policy when updating the Q-function. The core idea of SARSA is to update the Q function and strategy to obtain the optimal decision-making strategy by continuously interacting with the environment.
3️⃣ Deep reinforcement learning
Deep reinforcement learning is a method that combines deep learning and reinforcement learning. It uses deep neural networks to approximate value functions or policy functions to solve problems in high-dimensional state spaces and action spaces. Representative algorithms for deep reinforcement learning include Deep Q-Network (DQN), Proximal Policy Optimization (PPO), etc.
4️⃣Actor-Critic
Actor-Critic is a reinforcement learning algorithm that combines policy gradients and value functions. It learns a policy function (Actor) and a value function (Critic) at the same time, and updates the parameters through the estimation of the policy gradient method and the value function. The Actor-Critic algorithm can better deal with the problems of continuous action space and high-dimensional state space.
3. Application field
Reinforcement learning has a wide range of applications in various fields, and some of the common application areas are described below:
1️⃣ Games
In the field of games, reinforcement learning is used to train agents to play various types of games. For example, AlphaGo defeated the human world champion in Go through reinforcement learning. Reinforcement learning has also been used to train agents to play video games, such as Atari games.
2️⃣Robot control
In the field of robotic control, reinforcement learning is used to train robots to learn how to navigate and operate in complex environments. By interacting with the environment, robots can learn skills such as how to avoid obstacles and grasp objects.
3️⃣Automatic driving
In the field of autonomous driving, reinforcement learning is used to train self-driving vehicles to learn how to make optimal decisions. By interacting with the environment, self-driving vehicles can learn how to obey traffic rules, drive safely, and more.
4️⃣Financial transactions
In the field of financial transactions, reinforcement learning is used to train agents to learn how to make optimal trading decisions. By interacting with the market, the agent can learn how to predict market trends, optimize trading strategies, etc.
Four. Summary
The goal of reinforcement learning is to learn how to make optimal decisions by interacting with the environment. With the combination of deep learning and reinforcement learning, the ability of reinforcement learning to solve complex problems will continue to improve, bringing more possibilities for the development of artificial intelligence.
