Article Introduction：Reinforcement learning method for Vue component communication In Vue development, component communication is a very important topic. It involves how to share data between multiple components, trigger events, etc. A common approach is to use props and $emit methods for communication between parent and child components. However, this simple method of communication can become cumbersome and difficult to maintain when applications grow in size and the relationships between components become complex. Reinforcement learning is an algorithm that uses trial and error and reward mechanisms to optimize problem solving. In component communication, I

2023-07-17 comment 0  1269

Article Introduction：How to build a reinforcement learning algorithm using PHP Introduction: Reinforcement learning is a machine learning method that learns how to make optimal decisions by interacting with the environment. In this article, we will introduce how to build reinforcement learning algorithms using the PHP programming language and provide code examples to help readers better understand. 1. What is reinforcement learning algorithm? Reinforcement learning algorithm is a machine learning method that learns how to make decisions by observing feedback from the environment. Unlike other machine learning algorithms, reinforcement learning algorithms are not just based on existing data

2023-07-31 comment 0  703

Article Introduction：Now, robots can learn precision factory control tasks. In recent years, significant progress has been made in the field of robot reinforcement learning technology, such as quadruped walking, grasping, dexterous manipulation, etc., but most of them are limited to the laboratory demonstration stage. Widely applying robot reinforcement learning technology to actual production environments still faces many challenges, which to a certain extent limits its application scope in real scenarios. In the process of practical application of reinforcement learning technology, it is necessary to overcome multiple complex problems including reward mechanism setting, environment reset, sample efficiency improvement, and action safety guarantee. Industry experts emphasize that solving the many problems in the actual implementation of reinforcement learning technology is as important as the continuous innovation of the algorithm itself. Faced with this challenge, researchers from the University of California, Berkeley, Stanford University, the University of Washington, and

2024-02-21 comment 0  1193

Article Introduction：Q-Learning: Dealing with Exorbitant State-Action ValuesQ-Learning, a reinforcement learning technique, aims to derive optimal policies by...

2024-10-25 comment 0  741

Article Introduction：OpenRL is a PyTorch-based reinforcement learning research framework developed by the Fourth Paradigm reinforcement learning team. It supports the training of single-agent, multi-agent, natural language and other tasks. OpenRL is developed based on PyTorch, with the goal of providing the reinforcement learning research community with an easy-to-use, flexible, efficient, and sustainably scalable platform. Currently, the features supported by OpenRL include: a common interface that is easy to use and supports single-agent and multi-agent training; supports reinforcement learning training for natural language tasks (such as dialogue tasks); supports importing models and data from HuggingFace; supports LSTM, GRU, Models such as Transformer support a variety of training accelerations, such as: automatic mixed precision training,

2023-05-11 comment 0  1064

Article Introduction：From August 6th to 7th, 2022, the AISummit Global Artificial Intelligence Technology Conference will be held as scheduled. At the "Artificial Intelligence Frontier Exploration" sub-forum held on the afternoon of the 7th, Xishanju AI technical expert Huang Hongbo brought a theme sharing of "Practical Combination of Reinforcement Learning and Behavior Trees in Games" and shared in detail the impact of reinforcement learning in the game field. value. Huang Hongbo said that the implementation of reinforcement learning technology does not lie in changing the algorithm to be more powerful, but in combining reinforcement learning technology with deep learning and game planning to form a complete set of solutions and implement them. Reinforcement learning makes games more intelligent. The implementation of reinforcement learning in games can make games more intelligent and more playable. This is the use of reinforcement learning in games.

2023-04-09 comment 0  1822

Article Introduction：Reinforcement learning (RL) is a machine learning method in which agents learn through trial and error. Reinforcement learning algorithms are used in many fields, such as gaming, robotics, and finance. The goal of RL is to discover a strategy that maximizes expected long-term returns. Reinforcement learning algorithms are generally divided into two categories: model-based and model-free. Model-based algorithms use environmental models to plan optimal paths of action. This approach relies on accurate modeling of the environment and then using the model to predict the outcomes of different actions. In contrast, model-free algorithms learn directly from interactions with the environment and do not require explicit modeling of the environment. This method is more suitable for situations where the environment model is difficult to obtain or inaccurate. In actual comparison, model-free reinforcement learning algorithms do not

2024-03-19 comment 0  929

Article Introduction：Reinforcement learning (RL) is a machine learning method that allows an agent to learn how to behave in its environment through trial and error. Agents are rewarded or punished for taking actions that lead to desired outcomes. Over time, the agent learns to take actions that maximize its expected reward. RL agents are typically trained using a Markov decision process (MDP), a mathematical framework for modeling sequential decision problems. MDP consists of four parts: State: a set of possible states of the environment. Action: A set of actions that an agent can take. Transition function: A function that predicts the probability of transitioning to a new state given the current state and action. Reward function: A function that assigns a reward to the agent for each conversion. The agent's goal is to learn a policy function,

2023-10-31 comment 0  644

Article Introduction：AB testing is a technique widely used in online experiments. Its main purpose is to compare two or more versions of a page or application to determine which version achieves better business goals. These goals can be click-through rates, conversion rates, etc. In contrast, reinforcement learning is a machine learning method that uses trial-and-error learning to optimize decision-making strategies. Policy gradient reinforcement learning is a special reinforcement learning method that aims to maximize cumulative rewards by learning optimal policies. Both have different applications in optimizing business goals. In AB testing, we think of different page versions as different actions, and business goals can be thought of as important indicators of reward signals. In order to achieve maximum business goals, we need to design a strategy that can choose

2024-01-24 comment 0  991

Article Introduction：Large-scale generative models have brought huge breakthroughs to natural language processing and even computer vision in the past two years. Recently, this trend has also affected reinforcement learning, especially offline reinforcement learning (offline RL), such as Decision Transformer (DT)[1], Trajectory Transformer (TT)[2], Gato[3], Diffuser[4], etc. This method regards reinforcement learning data (including status, action, reward and return-to-go) as a string of destructured sequence data, and models these sequence data as the core task of learning. These models can all use supervised or self-supervised learning methods

2023-04-14 comment 0  959

Article Introduction：Parkour is an extreme sport. It is a huge challenge for robots, especially four-legged robot dogs, which need to quickly overcome various obstacles in complex environments. Some studies have attempted to use reference animal data or complex rewards, but these approaches generate parkour skills that are either diverse but blind, or vision-based but scene-specific. However, autonomous parkour requires robots to learn vision-based and diverse general skills to perceive various scenarios and respond quickly. Recently, a video of a robot dog parkour went viral. The robot dog in the video quickly overcomes various obstacles in various scenarios. For example, passing through the gap under the iron plate, climbing up a wooden box, and then jumping onto another wooden box, a series of actions are smooth and smooth: this series of actions shows that the robot dog has mastered crawling, climbing and crawling on the ground.

2023-09-20 comment 0  1091

Article Introduction：By integrating artificial intelligence technology into C++ graphics programming, developers can create more intelligent and interactive applications. These include image classification, object detection, image generation, game AI, path planning, scene generation and other functions. Artificial intelligence technologies such as neural networks, reinforcement learning, and generative adversarial networks can be integrated with C++ through frameworks such as TensorFlow, OpenAIGym, and PyTorch to realize these functions.

2024-06-02 comment 0  348

Article Introduction：With the development of artificial intelligence technology, reinforcement learning, as an important artificial intelligence technology, has been widely used in many fields, such as control systems, games, etc. As a popular programming language, Python also provides the implementation of many reinforcement learning algorithms. This article will introduce commonly used reinforcement learning algorithms and their characteristics in Python. Q-learningQ-learning is a reinforcement learning algorithm based on a value function. It guides behavioral strategies by learning a value function, allowing the agent to choose in the environment.

2023-06-04 comment 0  1408

Article Introduction：Previously, we introduced the application of sequence modeling methods based on Transformer and Diffusion Model in reinforcement learning, especially in the field of offline continuous control. Among them, Trajectory Transformer (TT) and Diffusser are model-based planning algorithms. They show very high-precision trajectory prediction and good flexibility, but the decision-making delay is relatively high. In particular, TT discretizes each dimension independently as a symbol in the sequence, which makes the entire sequence very long, and the time consuming of sequence generation increases rapidly with the dimensions of states and actions.

2023-04-13 comment 0  1641

Article Introduction：Since its release, the Transformer model has quickly become a mainstream neural architecture in supervised learning settings in the fields of natural language processing and computer vision. Although the craze of Transformer has begun to sweep across the field of reinforcement learning, due to the characteristics of RL itself, such as the need for unique features, architecture design, etc., the current combination of Transformer and reinforcement learning is not smooth, and its development path lacks relevant papers to conduct thorough analysis. To summarize. Recently, researchers from Tsinghua University, Peking University, and Tencent jointly published a research paper on the combination of Transformer and reinforcement learning, systematically reviewing the motivation and development process of using Transformer in reinforcement learning. paper

2023-04-13 comment 0  1108

Article Introduction：Recently, the quantitative platform family has welcomed a new member, an open source platform based on reinforcement learning: TradeMaster—Trading Master. Developed by Nanyang Technological University, TradeMaster is a unified, end-to-end, user-friendly quantitative trading platform covering four major financial markets, six major trading scenarios, 15 reinforcement learning algorithms and a series of visual evaluation tools! Platform address: https://github.com/TradeMaster-NTU/TradeMaster Background Introduction In recent years, artificial intelligence technology is occupying an increasingly important position in quantitative trading strategies. Due to its outstanding decision-making ability in complex environments, reinforcement learning technology is applied to

2023-04-11 comment 0  1073

Article Introduction：​Everyone has encountered an age-old problem. You're trying to pick a restaurant to eat at on a Friday night but don't have a reservation. Should you wait in line at your favorite restaurant that’s packed with people, or try a new restaurant in the hope of discovering some tastier surprises? The latter does have the potential to lead to surprises, but this kind of curiosity-driven behavior comes with risks: the food at that new restaurant you try might be even tastier. Curiosity is the driving force for AI to explore the world, and there are numerous examples - autonomous navigation, robot decision-making, optimized detection results, etc. In some cases, machines use "reinforcement learning" to accomplish a goal, in which the AI ​​agent repeatedly learns from being rewarded for good behavior and punished for bad behavior. Just like when humans choose a restaurant

2023-04-13 comment 0  990

Article Introduction：Chess has always been a proving ground for AI. 70 years ago, Alan Turing hypothesized that it would be possible to build a chess-playing machine that could learn on its own and continually improve from its own experience. “Deep Blue” that appeared in the last century defeated humans for the first time, but it relied on experts to encode human chess knowledge. AlphaZero, which was born in 2017, realized Turing’s conjecture as a neural network-driven reinforcement learning machine. AlphaZero does not need to use any hand-designed heuristics or watch humans play chess, but is trained entirely by playing against itself. So, does it really learn human concepts about chess? This is a neural network interpretability problem. In this regard, AlphaZero’s

2023-04-12 comment 0  1382

Article Introduction：Currently popular reinforcement learning algorithms include Q-learning, SARSA, DDPG, A2C, PPO, DQN, and TRPO. These algorithms have been used in various applications such as games, robots, and decision-making, and these popular algorithms are constantly being developed and improved. In this article, we will give a brief introduction to them. 1. Q-learningQ-learning: Q-learning is a model-free, non-strategy reinforcement learning algorithm. It estimates the optimal action value function using the Bellman equation, which iteratively updates the estimated value for a given state-action pair. Q-learning is known for its simplicity and ability to handle large continuous state spaces.

2023-04-11 comment 0  1628

Article Introduction：When it comes to reinforcement learning, many researchers’ adrenaline surges uncontrollably! It plays a very important role in game AI systems, modern robots, chip design systems and other applications. There are many different types of reinforcement learning algorithms, but they are mainly divided into two categories: "model-based" and "model-free". In a conversation with TechTalks, neuroscientist and author of "The Birth of Intelligence" Daeyeol Lee discussed different models of reinforcement learning in humans and animals, artificial intelligence and natural intelligence, and future research directions. Model-free reinforcement learning In the late 19th century, the "Law of Effect" proposed by psychologist Edward Thorndike became the basis of model-free reinforcement learning. Th

2023-05-09 comment 0  874