Tagged articles

reinforcement learning

822 articles · Page 8 of 9

Jul 28, 2022 · Artificial Intelligence

Unlocking Reinforcement Learning: Core Concepts, Algorithms, and Real‑World Applications

This article introduces reinforcement learning by defining agents, environments, rewards, and policies, explains key concepts such as Markov Decision Processes and Bellman equations, and surveys major algorithms—including dynamic programming, Monte‑Carlo, TD learning, policy gradients, Q‑learning, DQN, and evolution strategies—while highlighting practical challenges and notable case studies like AlphaGo Zero.

Evolution StrategiesMDPQ-Learning

0 likes · 27 min read

Unlocking Reinforcement Learning: Core Concepts, Algorithms, and Real‑World Applications

Youku Technology

Jul 5, 2022 · Artificial Intelligence

Enlarging the Long-time Dependencies via RL-based Memory Network in Movie Affective Analysis

The paper introduces a reinforcement‑learning‑driven memory network that stores and updates historical video information via DDPG, overcoming LSTM/Transformer limitations on long‑duration movie sequences, and achieves state‑of‑the‑art affective prediction on LIRIS‑ACCEDE and related datasets, with real‑world deployments in AI content inspection and film‑element knowledge graphs.

long-term dependenciesmemory networkmovie affective analysis

0 likes · 5 min read

Enlarging the Long-time Dependencies via RL-based Memory Network in Movie Affective Analysis

58 Tech

Jun 24, 2022 · Artificial Intelligence

Reinforcement Learning for Lead Generation in Task‑Oriented Dialogue Systems

This article presents a reinforcement‑learning‑based approach to improve lead‑capture efficiency of a task‑oriented chatbot used in local services, detailing the system architecture, RL algorithms (DQN/DDQN), data construction, model training, offline and online evaluation, and the resulting commercial gains.

Customer ServiceDQNLead Generation

0 likes · 27 min read

Reinforcement Learning for Lead Generation in Task‑Oriented Dialogue Systems

AntTech

Jun 22, 2022 · Cloud Computing

Meta Reinforcement Learning Framework for Predictive Autoscaling in Cloud Environments

This article presents a cloud-native, end‑to‑end autoscaling solution that integrates traffic forecasting, CPU utilization meta‑prediction, and a reinforcement‑learning‑based scaling decision module into a fully differentiable system, achieving higher resource utilization and cost efficiency as demonstrated by ACM SIGKDD 2022 research.

AutoscalingCapacity ManagementCloud Computing

0 likes · 10 min read

Meta Reinforcement Learning Framework for Predictive Autoscaling in Cloud Environments

DataFunSummit

Jun 21, 2022 · Artificial Intelligence

JiuGe: An Automatic Chinese Classical Poetry Generation System – Algorithms and Research Overview

This article presents the JiuGe system developed by THUNLP for automatically generating Chinese classical poetry, detailing its research motivations, model architecture—including salient‑clue, working‑memory, topic‑memory, style‑transfer and reinforcement‑learning components—implementation, applications, and future directions.

Artificial IntelligenceKnowledge GraphPoetry Generation

0 likes · 18 min read

JiuGe: An Automatic Chinese Classical Poetry Generation System – Algorithms and Research Overview

Huawei Cloud Developer Alliance

Jun 1, 2022 · Artificial Intelligence

How AI Beats Super Mario with PPO in 5 Minutes

This tutorial demonstrates how to use Huawei Cloud ModelArts and the Proximal Policy Optimization (PPO) reinforcement‑learning algorithm to train an AI agent that can clear most Super Mario levels within about 1500 episodes, even for users with no coding experience.

AIModelArtsPPO

0 likes · 6 min read

How AI Beats Super Mario with PPO in 5 Minutes

DataFunSummit

May 16, 2022 · Artificial Intelligence

Reinforcement Learning for E‑commerce Search Ranking: RNN User State Modeling and DDPG Long‑Term Value Optimization

This presentation details how JD applied reinforcement learning—using RNN‑based user state modeling and a DDPG framework—to improve e‑commerce search ranking by optimizing long‑term cumulative value, showing significant offline and online gains in conversion and GMV.

DDPGRNNUser Modeling

0 likes · 20 min read

Reinforcement Learning for E‑commerce Search Ranking: RNN User State Modeling and DDPG Long‑Term Value Optimization

Meituan Technology Team

Apr 28, 2022 · Artificial Intelligence

Multi-Action Computation Allocation via Evolutionary Strategies in Meituan Takeaway Advertising

This article analyzes Meituan's delivery advertising system, detailing the shift from linear programming to an evolutionary‑strategy‑based multi‑action allocation (ES‑MACA), describing problem formalization, offline training, reward evaluation, online decision flow, extensive offline and online experiments, and future directions toward reinforcement learning.

AdvertisingMeituanOnline Advertising

0 likes · 28 min read

Multi-Action Computation Allocation via Evolutionary Strategies in Meituan Takeaway Advertising

Code DAO

Apr 28, 2022 · Artificial Intelligence

Model-Based Reinforcement Learning from Raw Video: A Detailed Walkthrough

The article explains how to train robots to learn tasks directly from raw video using model-based reinforcement learning, covering POMDP formulation, CNN auto‑encoders, latent‑space representations, iLQR optimization, and a step‑by‑step pipeline with concrete examples and references.

CNN autoencoderPOMDPiLQR

0 likes · 11 min read

Model-Based Reinforcement Learning from Raw Video: A Detailed Walkthrough

Code DAO

Apr 24, 2022 · Artificial Intelligence

How Transfer Learning Accelerates Deep Learning Across Vision, NLP, and Reinforcement Learning

The article explains how transfer learning reduces data and time requirements in deep learning by reusing pretrained models for vision, natural language processing, and reinforcement learning, while discussing challenges such as overfitting, the need for progressive networks, entropy regularization, domain adaptation, multi‑task learning, and model distillation.

Domain AdaptationTransfer Learningdeep learning

0 likes · 10 min read

How Transfer Learning Accelerates Deep Learning Across Vision, NLP, and Reinforcement Learning

DaTaobao Tech

Apr 13, 2022 · Artificial Intelligence

Machine‑Learning Based Bandwidth Prediction and Adaptive Streaming for Taobao Live: Concerto, OnRL, and Loki

Alibaba’s Taobao Live team replaced rule‑based bandwidth estimators with three machine‑learning solutions—Concerto, OnRL, and Loki—trained on over a million hours of global live‑stream data, achieving up to 13% throughput gain, threefold stall reduction, and up to 44% lower 95th‑percentile stalls, now deployed commercially.

adaptive bitratebandwidth predictionmachine learning

0 likes · 14 min read

Machine‑Learning Based Bandwidth Prediction and Adaptive Streaming for Taobao Live: Concerto, OnRL, and Loki

Python Programming Learning Circle

Apr 6, 2022 · Artificial Intelligence

Building a DQN‑based Autonomous Driving Agent with highway‑env in Python

This tutorial explains how to install the gym and highway‑env packages, configure the simulation environment, process state and action representations, implement a DQN network in PyTorch, and train the model while visualizing performance metrics for autonomous driving tasks.

DQNPythonSimulation

0 likes · 11 min read

Building a DQN‑based Autonomous Driving Agent with highway‑env in Python

Alimama Tech

Mar 16, 2022 · Artificial Intelligence

Deep GSP: Multi‑Objective Deep Learning Based Advertising Auction Mechanism

Deep GSP is a multi‑objective, deep‑learning ad auction that jointly learns rank scores while enforcing game‑theoretic constraints—monotonicity, incentive compatibility, and Nash equilibrium—and a smooth‑transition penalty, using DDPG reinforcement learning to outperform traditional GSP across revenue, clicks, conversions, and add‑to‑cart metrics.

advertising auctionmechanism designmulti-objective optimization

0 likes · 18 min read

Deep GSP: Multi‑Objective Deep Learning Based Advertising Auction Mechanism

DataFunSummit

Mar 12, 2022 · Artificial Intelligence

Evolution of Re‑ranking Techniques in Kuaishou Short‑Video Recommendation System

This article details Kuaishou's short‑video recommendation pipeline, explaining the challenges of large‑scale sequencing, the development of sequence re‑ranking, multi‑content mixing, on‑device re‑ranking, and reinforcement‑learning‑based strategies, and demonstrates how these innovations improve user engagement and business metrics.

KuaishouRecommendation Systemsmulti-content mixing

0 likes · 15 min read

Evolution of Re‑ranking Techniques in Kuaishou Short‑Video Recommendation System

DataFunSummit

Mar 3, 2022 · Artificial Intelligence

Sequence Optimization, Context-Aware CTR Re-Estimation, and Session-Level Auction for JD Advertising Ranking

The article presents JD's technical evolution for advertising ranking, covering technology selection for recommendation ad sorting, context‑aware CTR re‑estimation, reinforcement‑learning‑based sequence optimization, and a session‑level auction mechanism that together improve monetization efficiency and long‑term user value.

CTRauctionreinforcement learning

0 likes · 18 min read

Sequence Optimization, Context-Aware CTR Re-Estimation, and Session-Level Auction for JD Advertising Ranking

DataFunTalk

Feb 24, 2022 · Artificial Intelligence

Sequence Optimization and Context-Aware CTR Re-Estimation for JD Advertising Ranking

The article presents JD's technical evolution for advertising ranking, covering recommendation ad sorting, context‑aware CTR re‑estimation, reinforcement‑learning‑based sequence optimization, and session‑level auction mechanisms, and includes a Q&A that highlights practical gains and implementation challenges.

AdvertisingCTR predictionContext-Aware

0 likes · 14 min read

Sequence Optimization and Context-Aware CTR Re-Estimation for JD Advertising Ranking

DataFunTalk

Feb 20, 2022 · Artificial Intelligence

Distilled Reinforcement Learning Framework for Recommendation (DRL-Rec): Design, Modules, and Experimental Evaluation

This article presents DRL-Rec, a distilled reinforcement learning framework for recommendation that integrates an exploring‑filtering module and confidence‑guided distillation to compress RL‑based recommenders while improving accuracy, and reports significant offline and online performance gains on a large‑scale system.

knowledge distillationonline experimentsreinforcement learning

0 likes · 16 min read

Distilled Reinforcement Learning Framework for Recommendation (DRL-Rec): Design, Modules, and Experimental Evaluation

DataFunTalk

Feb 10, 2022 · Artificial Intelligence

Evolution of Re‑ranking Techniques in Kuaishou Short‑Video Recommendation System

This article details the technical evolution of Kuaishou's short‑video recommendation pipeline, focusing on sequence re‑ranking, multi‑content mixing, and on‑device re‑ranking, and explains how transformer‑based models, generator‑evaluator frameworks, and reinforcement‑learning strategies are employed to maximize overall sequence value, user engagement, and revenue.

KuaishouRe‑rankingSequence Modeling

0 likes · 15 min read

IEG Growth Platform Technology Team

Jan 10, 2022 · Artificial Intelligence

Applying Reinforcement Learning to Optimize Advertising Bidding ROI

This article presents a comprehensive overview of using reinforcement learning to solve advertising bidding ROI optimization, covering historical foundations, methodological reasoning, system architecture, practical implementation details, challenges, evaluation metrics, and recommended algorithms for real‑world ad placement scenarios.

AdvertisingOnline AdvertisingROI optimization

0 likes · 17 min read

Applying Reinforcement Learning to Optimize Advertising Bidding ROI

DataFunTalk

Jan 3, 2022 · Artificial Intelligence

Intelligent Advertising Delivery System: Budget‑Constrained Bidding, Multi‑Constraint Bidding, Sequential Allocation, and Multi‑Channel Optimization

This article systematically introduces Alibaba's advertising intelligence platform, covering the evolution from simple CPM/CPC models to advanced budget‑constrained, multi‑constraint, and sequential bidding strategies, multi‑channel optimization, and reinforcement‑learning‑based solutions that jointly maximize advertiser ROI and platform revenue.

Multi-Channelbudget optimizationmachine learning

0 likes · 34 min read

Intelligent Advertising Delivery System: Budget‑Constrained Bidding, Multi‑Constraint Bidding, Sequential Allocation, and Multi‑Channel Optimization

58 Tech

Dec 28, 2021 · Artificial Intelligence

Reinforcement Learning for Cold‑Start Job Recommendation in 58.com

This talk explains how 58.com tackles the cold‑start and interest‑divergence problems of its massive blue‑collar job recruitment platform by modeling the recommendation process as a reinforcement‑learning task, detailing the use of multi‑armed bandit, contextual bandit, and linear‑UCB algorithms, offline evaluation pipelines, online deployment, and observed performance gains.

Contextual Banditcold startjob recommendation

0 likes · 25 min read

Reinforcement Learning for Cold‑Start Job Recommendation in 58.com

DataFunTalk

Dec 17, 2021 · Artificial Intelligence

Applying Reinforcement Learning to Solve Cold‑Start Problems in 58.com Job Recruitment

This talk explains how 58.com’s massive blue‑collar recruitment platform uses reinforcement‑learning techniques—including multi‑armed bandits, contextual MAB, and linear UCB—to address cold‑start and interest‑divergence challenges, describes the system architecture, offline evaluation, online deployment, and reports an 8% uplift in new‑user conversion.

cold startcontextual MABjob recruitment

0 likes · 26 min read

Applying Reinforcement Learning to Solve Cold‑Start Problems in 58.com Job Recruitment

Code DAO

Dec 14, 2021 · Artificial Intelligence

Building a Chess AI from Scratch: Combining AlphaZero and Transformers (Part 2)

This article walks through constructing a learnable chess AI by integrating AlphaZero‑style Monte Carlo Tree Search with a decoder‑only Transformer, detailing the game tree logic, model architecture, input and output encodings, self‑play training loop, and code implementation in PyTorch.

AlphaZeroMonteCarloTreeSearchPyTorch

0 likes · 23 min read

Building a Chess AI from Scratch: Combining AlphaZero and Transformers (Part 2)

IEG Growth Platform Technology Team

Dec 6, 2021 · Artificial Intelligence

Model-Free Reinforcement Learning for ROI Optimization: Methods, Advertising Applications, and Tencent Game Advertising Practice

This article introduces model‑free reinforcement learning fundamentals, reviews mainstream solution methods such as Monte‑Carlo, Temporal‑Difference, and n‑step TD with eligibility traces, discusses their application in online advertising and presents Tencent's game advertising practice, including algorithm choices, reward design, and experimental results.

A3CAdvertisingPPO

0 likes · 17 min read

Model-Free Reinforcement Learning for ROI Optimization: Methods, Advertising Applications, and Tencent Game Advertising Practice

Code DAO

Dec 3, 2021 · Artificial Intelligence

Understanding Actor‑Critic and A2C: From Policy Gradients to REINFORCE in RL

This article derives the policy‑gradient objective for discrete actions, implements the Monte‑Carlo REINFORCE algorithm in PyTorch, explains the actor‑critic framework, introduces Advantage Actor‑Critic (A2C) versus A3C, and demonstrates their performance on the OpenAI Gym CartPole‑v0 environment.

A2COpenAI GymPython

0 likes · 13 min read

Understanding Actor‑Critic and A2C: From Policy Gradients to REINFORCE in RL

Code DAO

Nov 28, 2021 · Artificial Intelligence

Adapting Soft Actor‑Critic for Discrete Action Spaces in Deep Reinforcement Learning

This article explains how to modify the Soft Actor‑Critic (SAC) algorithm—originally designed for continuous actions—to work with discrete action environments, presents the required changes to the actor and critic loss functions, provides a full PyTorch implementation, and evaluates the method on the CartPole‑v1 benchmark.

CartPoleDiscrete ActionsEntropy Regularization

0 likes · 20 min read

Adapting Soft Actor‑Critic for Discrete Action Spaces in Deep Reinforcement Learning

ByteDance Terminal Technology

Oct 26, 2021 · Mobile Development

Fastbot: Cross‑Platform Intelligent Automated Testing System for Android and iOS

This article details ByteDance’s Fastbot system, an AI‑driven cross‑platform automated testing framework for Android and iOS that leverages model‑based testing, reinforcement learning, and image‑based UI analysis to improve test coverage, fault injection, and scalability across mobile applications and games.

AIMobile Testingcross‑platform

0 likes · 36 min read

Fastbot: Cross‑Platform Intelligent Automated Testing System for Android and iOS

Alimama Tech

Sep 29, 2021 · Artificial Intelligence

Unified Solution to Constrained Bidding in Online Display Advertising (USCB)

The paper proposes a unified solution for real‑time bidding in online display ads that formulates advertiser budget and KPI limits as a constrained linear program, derives a closed‑form optimal bidding function with m+1 parameters, and uses model‑free reinforcement learning to dynamically adjust those parameters, achieving superior traffic‑value capture in large‑scale deployment on Alibaba’s Taobao platform.

constrained optimizationparameter tuningreal-time bidding

0 likes · 11 min read

Unified Solution to Constrained Bidding in Online Display Advertising (USCB)

Python Programming Learning Circle

Sep 27, 2021 · Artificial Intelligence

Training Reinforcement Learning Agents on Street Fighter III Using a MAME Wrapper Python Library

This tutorial explains how to install and use a Python library that wraps the MAME emulator to train reinforcement‑learning agents on arcade games such as Street Fighter III, covering system requirements, installation, environment configuration, debugging, step‑wise simulation, and a simple ConvNet agent example.

AIMAMEPython

0 likes · 4 min read

Training Reinforcement Learning Agents on Street Fighter III Using a MAME Wrapper Python Library

ByteFE

Aug 2, 2021 · Artificial Intelligence

An Overview of Artificial Intelligence, Machine Learning, and Neural Networks

This article provides a beginner‑friendly overview of artificial intelligence, its relationship with machine learning, the four major learning paradigms—supervised, unsupervised, semi‑supervised and reinforcement learning—along with a historical sketch of neural networks, their training workflow, loss functions, back‑propagation, and parameter‑update mechanisms, while also containing a brief recruitment notice.

Artificial IntelligenceSupervised LearningUnsupervised Learning

0 likes · 18 min read

An Overview of Artificial Intelligence, Machine Learning, and Neural Networks

DataFunSummit

Aug 1, 2021 · Artificial Intelligence

A Comprehensive Overview of Sequence Recommendation Models and Techniques

This article provides an in‑depth review of user behavior sequence recommendation, covering problem definition, data preparation, and a range of neural models—including MLP, CNN, RNN, Temporal CNN, self‑attention, and reinforcement learning—along with practical implementation tips and references.

MLneural networksreinforcement learning

0 likes · 35 min read

A Comprehensive Overview of Sequence Recommendation Models and Techniques

DataFunSummit

Jul 25, 2021 · Artificial Intelligence

Advances in Query Understanding and Semantic Retrieval at Zhihu Search

This article details Zhihu Search's engineering solutions for long‑tail query challenges, covering historical development, term weighting, synonym expansion, query rewriting with reinforcement learning, and semantic recall using BERT‑based models, while also outlining future research directions such as GAN‑based rewriting and lightweight pre‑training.

BERTEmbedding RetrievalQuery Rewriting

0 likes · 14 min read

Advances in Query Understanding and Semantic Retrieval at Zhihu Search

Java Architect Essentials

Jul 21, 2021 · Artificial Intelligence

DouZero: A Simple Monte‑Carlo Based AI that Achieves State‑of‑the‑Art Performance in Dou Dizhu

DouZero, a reinforcement‑learning AI for the Chinese card game Dou Dizhu, combines a Monte‑Carlo value‑network with compact action encoding, trains on a four‑GPU server, and outperforms existing AI baselines, ranks first on Botzone, and even surpasses human play in several metrics.

AIDouZeroMonte Carlo

0 likes · 15 min read

DouZero: A Simple Monte‑Carlo Based AI that Achieves State‑of‑the‑Art Performance in Dou Dizhu

DataFunTalk

Jun 15, 2021 · Artificial Intelligence

Personalized Approximate Pareto-Efficient Recommendation (PAPERec): A Multi‑Objective Reinforcement Learning Framework for User‑Level Objective Personalization

The paper introduces PAPERec, a personalized multi‑objective recommendation framework that leverages Pareto‑oriented reinforcement learning to generate user‑specific objective weights, enabling the model to approximate Pareto‑optimal solutions and achieve superior click‑through rate and dwell‑time performance in both offline and online experiments.

CTRPareto efficiencyRecommendation Systems

0 likes · 12 min read

Personalized Approximate Pareto-Efficient Recommendation (PAPERec): A Multi‑Objective Reinforcement Learning Framework for User‑Level Objective Personalization

Alimama Tech

Jun 10, 2021 · Artificial Intelligence

Overview of Recent Alibaba Mama Research Papers Presented at KDD 2021 on Advertising and AI

At KDD 2021, Alibaba Mama presented six papers that introduced a unified constrained‑bidding solution, a deep‑learnable auction mechanism, real‑negative training for delayed‑feedback CVR, a contextual‑bandit advertising strategy recommender, a multi‑agent cooperative bidding game, and an uncertainty‑aware exploration model, all of which have been deployed to boost platform revenue and advertiser performance.

AlibabaAuction MechanismsKDD

0 likes · 16 min read

Overview of Recent Alibaba Mama Research Papers Presented at KDD 2021 on Advertising and AI

Laiye Technology Team

Jun 8, 2021 · Artificial Intelligence

Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue Systems

This paper presents a hierarchical reinforcement learning approach that jointly trains dialogue policy and natural language generation modules for task-oriented dialogue systems, achieving state‑of‑the‑art performance on MultiWOZ 2.0 and 2.1 while preserving response fluency.

MultiWOZNatural Language Generationdialogue policy

0 likes · 10 min read

Modelling Hierarchical Structure between Dialogue Policy and Natural Language Generator with Option Framework for Task-oriented Dialogue Systems

DataFunTalk

Apr 24, 2021 · Artificial Intelligence

Intelligent Advertising Delivery System and Techniques: From Budget‑Constrained Bidding to Multi‑Channel Optimization

This article systematically introduces Alibaba's advertising intelligence platform, covering the evolution from basic CPM/CPC models to advanced OCPC/OCPM, budget‑constrained bidding, multi‑constraint bidding, sequence‑based long‑term value bidding, multi‑channel allocation, and the AI‑driven Smart Bidding product, highlighting algorithmic foundations, practical implementations, and performance gains.

AdvertisingMulti-Channelbidding

0 likes · 32 min read

Intelligent Advertising Delivery System and Techniques: From Budget‑Constrained Bidding to Multi‑Channel Optimization

DataFunSummit

Mar 25, 2021 · Artificial Intelligence

An Overview of Reinforcement Learning: Concepts, Applications, Challenges, and Future Prospects

Reinforcement learning, a branch of artificial intelligence, is explained through its core concepts, successful case studies such as AlphaGo and AlphaStar, practical application workflows, current challenges, resources, and future outlook, offering a comprehensive guide for researchers and practitioners.

ApplicationsArtificial IntelligencePolicy Optimization

0 likes · 56 min read

An Overview of Reinforcement Learning: Concepts, Applications, Challenges, and Future Prospects

DataFunTalk

Mar 9, 2021 · Artificial Intelligence

Introduction to Common Machine Learning Algorithms with Python Implementations

This article introduces the three main categories of machine learning—supervised, unsupervised, and reinforcement learning—detailing common algorithms such as Linear Regression, Logistic Regression, Naive Bayes, K‑Nearest Neighbors, Decision Trees, Random Forests, SVM, K‑Means, and PCA, and provides concise Python code examples using scikit‑learn for each.

PythonUnsupervised Learningmachine learning

0 likes · 18 min read

Introduction to Common Machine Learning Algorithms with Python Implementations

DataFunTalk

Feb 24, 2021 · Artificial Intelligence

Multi‑Objective Ranking in Kuaishou Short‑Video Recommendation: System Design and Online Results

This article details Kuaishou's multi‑objective ranking pipeline for short‑video recommendation, covering manual score fusion, GBDT ensemble, Learn‑to‑Rank, online auto‑tuning, ensemble sorting, reinforcement‑learning rerank, and on‑device rerank, and reports their impact on DAU, watch time and user interaction.

Kuaishoumachine learningmulti-objective ranking

0 likes · 21 min read

Multi‑Objective Ranking in Kuaishou Short‑Video Recommendation: System Design and Online Results

Architects' Tech Alliance

Jan 29, 2021 · Artificial Intelligence

Comprehensive Overview of Machine Learning: Types, Industry Chain, and Key Technologies

This article provides a detailed introduction to machine learning, covering its definition, learning modes such as supervised, unsupervised and reinforcement learning, shallow versus deep learning, the full industry chain from AI chips to cloud and big‑data services, and the major open‑source frameworks and platforms driving the field.

AI chipsBig DataSupervised Learning

0 likes · 11 min read

Comprehensive Overview of Machine Learning: Types, Industry Chain, and Key Technologies

Programmer DD

Jan 3, 2021 · Artificial Intelligence

How Self‑Play and GAIL Powered the WeKick AI to Win the First Google Football Kaggle Championship

After a nostalgic gaming session, the author recounts how Tencent’s upgraded AI, WeKick, leveraged self‑play reinforcement learning, GAIL‑based adversarial simulation, and a multi‑style League framework to dominate the inaugural Google Football Kaggle competition, illustrating the escalating complexity of multi‑agent AI in real‑time strategy games.

GAILKaggle competitionSelf-Play

0 likes · 8 min read

How Self‑Play and GAIL Powered the WeKick AI to Win the First Google Football Kaggle Championship

DataFunTalk

Dec 23, 2020 · Artificial Intelligence

Advances in Knowledge Graph Completion: Methods, Challenges, and Future Directions

This article reviews the rapid progress of knowledge graph completion, covering its background, formal problem definition, major technical approaches—including representation learning, path‑based search, reinforcement learning, logical reasoning, and meta‑learning—while discussing their challenges, recent improvements, and promising future research directions.

CompletionKnowledge GraphLogical Reasoning

0 likes · 14 min read

Advances in Knowledge Graph Completion: Methods, Challenges, and Future Directions

JD Cloud Developers

Dec 21, 2020 · Artificial Intelligence

Weekly Tech Highlights: AI Chip, Cloud Forecasts, Docker M1 Preview & More

This week’s developer newsletter spotlights the Chinese Academy of Sciences’ pioneering GNN accelerator chip, IDC’s ten cloud computing predictions for China, the booming IoT market and 5G dominance, Docker’s M1‑compatible desktop preview, a carbon‑nanotube transistor breakthrough, IBM’s FHE initiative, and recent AI research on lifelong learning and reinforcement learning exploration.

Artificial IntelligenceDockerHardware Acceleration

0 likes · 7 min read

Weekly Tech Highlights: AI Chip, Cloud Forecasts, Docker M1 Preview & More

DataFunTalk

Nov 12, 2020 · Artificial Intelligence

Reinforcement Learning for Recommendation System Mixing: Concepts, Practice, and Evaluation

This article explains how reinforcement learning, with its focus on maximizing long‑term reward, can improve recommendation system mixing by covering basic RL concepts, differences from supervised learning, multi‑armed bandit approaches, practical OpenAI Gym experiments, new AUC metrics, online gains, and advanced model optimizations.

Artificial IntelligenceOpenAI GymQ-Learning

0 likes · 10 min read

Reinforcement Learning for Recommendation System Mixing: Concepts, Practice, and Evaluation

Didi Tech

Oct 10, 2020 · Artificial Intelligence

Deep Reinforcement Learning for Route Planning in DiDi Ride‑Hailing

DiDi’s route engine, handling over 40 billion daily requests, replaces static graph algorithms with a deep‑reinforcement‑learning system that first learns intersection decisions via behavior‑cloning LSTM models and then refines them through self‑play Q‑learning, using beam‑search decoding to produce globally optimal, low‑deviation routes for ride‑hailing.

AIbeam searchbehavior cloning

0 likes · 12 min read

Deep Reinforcement Learning for Route Planning in DiDi Ride‑Hailing

DataFunTalk

Oct 4, 2020 · Artificial Intelligence

Reinforcement Learning for Product Ranking: Model Design, Experiments, and Online Deployment

This article presents a comprehensive study of using reinforcement learning to improve e‑commerce product ranking, covering the limitations of traditional scoring, the design of context‑aware models, a pointer‑network based sequence generator, various RL algorithms, extensive offline evaluations, and successful online deployment with future research directions.

PPOdeep learninge-commerce

0 likes · 28 min read

Reinforcement Learning for Product Ranking: Model Design, Experiments, and Online Deployment

Sohu Tech Products

Sep 16, 2020 · Artificial Intelligence

Open-Domain Dialogue Systems: Current State, Challenges, and Future Directions

This article reviews the latest advances in open-domain dialogue systems, covering classification, end‑to‑end generation challenges, knowledge‑controlled generation, automated evaluation, large‑scale latent‑space models such as PLATO, and outlines future research directions for building more coherent and controllable conversational AI.

Dialogue Systemsevaluationknowledge grounding

0 likes · 14 min read

Open-Domain Dialogue Systems: Current State, Challenges, and Future Directions

MaGe Linux Operations

Sep 9, 2020 · Artificial Intelligence

Master Machine Learning Basics: Concepts, Types, Algorithms & K‑NN Walkthrough

This comprehensive tutorial introduces machine learning fundamentals, its history, differences from traditional programming, key characteristics, and why Python is the preferred language, then explores supervised, unsupervised, and reinforcement learning, popular algorithms, detailed K‑Nearest Neighbors examples for classification and regression, and the essential steps to build and evaluate models.

KNNPythonSupervised Learning

0 likes · 21 min read

Master Machine Learning Basics: Concepts, Types, Algorithms & K‑NN Walkthrough

Programmer DD

Aug 31, 2020 · Artificial Intelligence

AI Fighter Falco Beats Human Pilot in Simulated Dogfight – Implications for Military AI

DARPA’s ACE program showcased the AI‑driven fighter Falco, built with the open‑source AdeptRL reinforcement‑learning framework, which defeated an experienced US Air Force instructor in a 1‑v‑1 simulated F‑16 dogfight, highlighting both the promise and current limitations of autonomous combat systems.

AIDARPASimulation

0 likes · 7 min read

AI Fighter Falco Beats Human Pilot in Simulated Dogfight – Implications for Military AI

DataFunTalk

Aug 15, 2020 · Artificial Intelligence

Dynamic Knapsack Optimization for Multi‑Channel Sequential Advertising Using Long‑Term Value

The article presents a novel multi‑channel sequential advertising framework that models budget‑constrained GMV optimization as a dynamic knapsack problem, introduces a long‑term value‑based RL solution (MSBCB), and validates its superiority through extensive offline and online experiments showing up to 10% ROI improvement.

Advertisingbudget optimizationdynamic knapsack

0 likes · 16 min read

Dynamic Knapsack Optimization for Multi‑Channel Sequential Advertising Using Long‑Term Value

Aotu Lab

Jul 22, 2020 · Frontend Development

How Q‑Learning Can Power Smart UI Testing and Scalable Pop‑ups with Puppeteer

This article explains how reinforcement‑learning (Q‑learning) can generate mock interface data for regression testing, how Puppeteer automates UI interactions, and how a DSL‑plus‑runtime approach enables scalable pop‑up components, reducing testing costs in complex e‑commerce interactions.

AutomationFrontend TestingPuppeteer

0 likes · 8 min read

How Q‑Learning Can Power Smart UI Testing and Scalable Pop‑ups with Puppeteer

DataFunTalk

Jul 21, 2020 · Artificial Intelligence

WeChat "Look" Recommendation System: Architecture, Modeling, and Engineering Challenges

This article details the end‑to‑end technical architecture of WeChat's "Look" personalized recommendation service, covering data collection, recall, multi‑stage ranking, various CTR and multi‑objective models, reinforcement‑learning based mixing, diversity optimization, and the engineering hurdles overcome to deploy these solutions at massive scale.

CTR predictionWeChat AIdeep learning

0 likes · 17 min read

WeChat "Look" Recommendation System: Architecture, Modeling, and Engineering Challenges

58 Tech

Jul 8, 2020 · Artificial Intelligence

Budget Pacing Techniques and Their Application in 58.com Advertising Platform

This article introduces mainstream budget‑pacing methods for cost‑per‑click online ads, describes the 58.com business scenarios, details the pacing algorithm—including bid modification, probabilistic throttling, and reinforcement‑learning approaches—explains system design with PID control, and presents online experimental results and future directions.

Ad TechBudget PacingOnline Advertising

0 likes · 14 min read

Budget Pacing Techniques and Their Application in 58.com Advertising Platform

Taobao Frontend Technology

Jun 30, 2020 · Frontend Development

How Reinforcement Learning Powers Front‑End Testing for Alibaba’s 618 Interactive Game

This article explains how the Taobao front‑end team tackled the complexity of the 618 interactive game by using reinforcement‑learning‑driven intelligent testing, Puppeteer‑based automated regression, and a decoupled UI‑logic architecture for scalable popup production, dramatically improving development efficiency and stability.

PuppeteerUI logic decouplingautomated testing

0 likes · 10 min read

How Reinforcement Learning Powers Front‑End Testing for Alibaba’s 618 Interactive Game

HomeTech

Jun 10, 2020 · Artificial Intelligence

Exploitation & Exploration Algorithms in Recommender Systems: ε‑Greedy, UCB, and Thompson Sampling Applications

This article introduces recommender systems and the exploitation‑exploration dilemma, explains common E&E algorithms such as ε‑greedy, Upper‑Confidence‑Bound, and Thompson Sampling, and details their practical deployment for interest‑point eviction, selection, and adaptive recall count optimization in an automotive recommendation platform.

Bandit AlgorithmsEpsilon-GreedyExploitation

0 likes · 10 min read

Exploitation & Exploration Algorithms in Recommender Systems: ε‑Greedy, UCB, and Thompson Sampling Applications

DataFunTalk

May 15, 2020 · Artificial Intelligence

Optimizing Sparse Feature Embedding for Large‑Scale Recommendation and CTR Prediction

The article reviews recent research on representing massive sparse features in click‑through‑rate (CTR) models, introducing Alibaba's Res‑embedding method and Google's Neural Input Search (NIS) approach, and discusses how these techniques improve embedding efficiency and model generalization in large‑scale recommendation systems.

CTR predictionRecommendation Systemsdeep learning

0 likes · 10 min read

Optimizing Sparse Feature Embedding for Large‑Scale Recommendation and CTR Prediction

JD Retail Technology

May 13, 2020 · Artificial Intelligence

JD's Two Papers Accepted at IJCAI2020 and SIGIR2020: Hierarchical Reinforcement Learning for Multi‑Goal Recommendation and Attention‑Based pCVR Prediction

JD announced that two of its research papers—one on a hierarchical reinforcement‑learning framework for multi‑objective recommendation (MaHRL) and another on an attention‑based model for delayed‑feedback conversion‑rate prediction (pCVR)—were accepted as full papers at the prestigious IJCAI2020 and SIGIR2020 conferences, highlighting the company's strong AI capabilities.

Artificial IntelligenceRecommendation Systemsconversion rate prediction

0 likes · 6 min read

JD's Two Papers Accepted at IJCAI2020 and SIGIR2020: Hierarchical Reinforcement Learning for Multi‑Goal Recommendation and Attention‑Based pCVR Prediction

Alibaba Cloud Developer

May 11, 2020 · Artificial Intelligence

How Reinforcement Learning Revolutionizes E‑commerce Product Ranking

This article details the evolution of AliExpress product ranking from simple DNN scoring to advanced reinforcement‑learning re‑ranking, comparing multiple models, exploring context effects, introducing pointer‑network generators, evaluating various RL algorithms, and reporting significant online gains in conversion and GMV.

e-commerceonline experimentsproduct ranking

0 likes · 28 min read

How Reinforcement Learning Revolutionizes E‑commerce Product Ranking

Alibaba Cloud Developer

Apr 24, 2020 · Artificial Intelligence

How Reinforcement Learning Can Supercharge New Media Marketing Strategies

This article examines the limitations of traditional new media marketing, explains reinforcement learning fundamentals, and presents a six‑step technical solution—including problem modeling, algorithm selection, action, state, reward design, and model training—that uses RL to optimize budget allocation and achieve over 35% improvement in campaign effectiveness while reducing costs.

AIbudget optimizationdigital advertising

0 likes · 20 min read

How Reinforcement Learning Can Supercharge New Media Marketing Strategies

360 Quality & Efficiency

Apr 17, 2020 · Artificial Intelligence

Extending APEX for Real Distributed Reinforcement Learning with tf2rl

The article examines the limitations of the single‑machine APEX framework in the tf2rl reinforcement‑learning library, proposes a cross‑machine distributed architecture using middleware such as Redis, compares alternative frameworks like EasyRL, and outlines expected performance gains and future development plans.

APEXDistributed TrainingOff-Policy

0 likes · 5 min read

Extending APEX for Real Distributed Reinforcement Learning with tf2rl

DataFunTalk

Apr 12, 2020 · Artificial Intelligence

Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems

In this article, Wang Zhe addresses fifteen common questions about recommendation systems, covering topics such as building cross‑domain knowledge, the role of deep reinforcement learning, handling sparse or low‑sample data, offline‑online evaluation, knowledge graphs, graph neural networks, model interpretability, large‑scale ID embedding, and career advice for engineers.

Graph Neural NetworkKnowledge GraphModel Evaluation

0 likes · 14 min read

Wang Zhe’s Machine Learning Notes – Answers to Frequently Asked Questions on Recommendation Systems

DataFunTalk

Mar 27, 2020 · Artificial Intelligence

Understanding Data Product Layers: Business Value, Data, Algorithms, and Applications

The article explains how data products create business value through application, data, and algorithm layers, using examples like 5G infrared temperature screening and ImageNet, and discusses the roles of experimental design, causal inference, and reinforcement learning in building effective AI‑driven strategies.

Artificial IntelligenceBusiness ValueData Product

0 likes · 8 min read

Understanding Data Product Layers: Business Value, Data, Algorithms, and Applications

Alibaba Cloud Developer

Feb 14, 2020 · Artificial Intelligence

How Alibaba’s AI Voice Bots Revolutionized Customer Service During the Pandemic

This article explains how Alibaba leveraged AI‑powered voice robots to handle massive outbound call volumes during COVID‑19, detailing the technology stack, real‑world application scenarios across finance and retail, and the future potential of intelligent voice assistants in customer service.

AICustomer Servicenatural language processing

0 likes · 11 min read

How Alibaba’s AI Voice Bots Revolutionized Customer Service During the Pandemic

Alibaba Cloud Developer

Feb 7, 2020 · Artificial Intelligence

Tackling Scalability, Data Scarcity, and Training Efficiency in Dialogue Management Models

This article reviews the evolution of dialogue management models from rule‑based systems to deep‑learning approaches, identifies three major challenges—poor scalability, limited annotated data, and low training efficiency—and surveys recent research solutions including semantic matching, knowledge distillation, hierarchical reinforcement learning, model‑based RL, and human‑in‑the‑loop methods.

Conversational AIdata annotationdialogue management

0 likes · 44 min read

Tackling Scalability, Data Scarcity, and Training Efficiency in Dialogue Management Models

Qunar Tech Salon

Feb 5, 2020 · Operations

Understanding Didi's Ride‑Hailing Dispatch Algorithms: Challenges, Models, and Future Directions

The article explains why Didi needs advanced dispatch algorithms, describes the complexities of order‑driver matching from simple one‑to‑one cases to large‑scale bipartite matching, and introduces batch matching, supply‑demand prediction, chain dispatch, and AI‑driven optimizations that together improve global efficiency and user experience.

AIDispatchRide Hailing

0 likes · 16 min read

Understanding Didi's Ride‑Hailing Dispatch Algorithms: Challenges, Models, and Future Directions

Top Architect

Jan 16, 2020 · Artificial Intelligence

A Survey of Neural Architecture Search: Search Spaces, Optimization Strategies, and Recent Results

This article surveys neural architecture search, classifying existing methods, describing common search spaces—including global and cell‑based designs—detailing optimization strategies such as reinforcement learning, evolutionary algorithms, surrogate models, one‑shot and differentiable approaches, and highlighting recent results and trends in the field.

Evolutionary AlgorithmsNASNeural Architecture Search

0 likes · 13 min read

A Survey of Neural Architecture Search: Search Spaces, Optimization Strategies, and Recent Results

DataFunTalk

Jan 2, 2020 · Artificial Intelligence

Improving Zhihu Search: Query Understanding, Term Weighting, Synonym Expansion, Query Rewriting, and Semantic Retrieval

This article details Zhihu's search engineering advances over the past year, covering long‑tail query challenges, term‑weight calculation, synonym expansion, query rewriting with translation models and reinforcement learning, and semantic retrieval using BERT‑based embeddings, while outlining future research directions.

NLPQuery Rewritingquery understanding

0 likes · 14 min read

Improving Zhihu Search: Query Understanding, Term Weighting, Synonym Expansion, Query Rewriting, and Semantic Retrieval

DataFunTalk

Dec 16, 2019 · Artificial Intelligence

A Comprehensive Overview of Sequential Recommendation Models and Techniques

This article provides an in-depth overview of sequential recommendation, defining the problem, discussing data preparation, and reviewing various neural architectures—including MLP, CNN, RNN, Temporal CNN, self‑attention, and reinforcement‑learning approaches—while offering practical guidance on model selection and implementation.

CNNRNNSequential Modeling

0 likes · 36 min read

A Comprehensive Overview of Sequential Recommendation Models and Techniques

DataFunTalk

Dec 10, 2019 · Artificial Intelligence

Applying Deep Reinforcement Learning (DQN) to the 2048 Game: Experiments and Insights

This article details a series of reinforcement‑learning experiments on the 2048 game, from random baselines through DQN implementations, classical value‑iteration methods, network redesigns, and Monte‑Carlo tree search, highlighting challenges such as reward design, over‑estimation, and exploration while achieving scores up to 34 000 and tiles of 2048.

2048AIDQN

0 likes · 8 min read

Applying Deep Reinforcement Learning (DQN) to the 2048 Game: Experiments and Insights

DataFunTalk

Nov 27, 2019 · Artificial Intelligence

Applying Reinforcement Learning and Graph Embedding for Intelligent User Operations in Didi Ride‑Sharing

This article describes how Didi Ride‑Sharing leverages reinforcement learning and graph‑embedding techniques to model and optimize user‑operation marketing, detailing system architecture, algorithm design, experimental ROI improvements, and personalized message delivery for enhanced conversion and cost efficiency.

DidiROIgraph embedding

0 likes · 11 min read

Applying Reinforcement Learning and Graph Embedding for Intelligent User Operations in Didi Ride‑Sharing

AntTech

Oct 30, 2019 · Artificial Intelligence

Financial Graph Machine Learning, AutoML, and Multi‑Agent Reinforcement Learning at Ant Financial

Professor Song Le presented at the Cloudwise Conference how Ant Financial leverages large‑scale graph neural networks, automated machine‑learning platforms, and multi‑agent reinforcement learning to model complex financial networks, improve risk control, and drive diverse fintech applications.

Ant FinancialLarge-Scale Graphgraph neural networks

0 likes · 12 min read

Financial Graph Machine Learning, AutoML, and Multi‑Agent Reinforcement Learning at Ant Financial

DataFunTalk

Oct 25, 2019 · Artificial Intelligence

Advances and Challenges in Human‑Machine Dialogue: Open‑Domain and Task‑Oriented Systems

This article reviews recent progress and open research problems in human‑machine dialogue, covering both open‑domain chat and task‑oriented systems, with focus on reply quality, decoding, retrieval‑augmented generation, controllable and personalized responses, multi‑turn modeling, reinforcement‑learning strategies, low‑resource NLU, and data augmentation techniques.

Dialogue SystemsResponse Generationnatural language processing

0 likes · 16 min read

Advances and Challenges in Human‑Machine Dialogue: Open‑Domain and Task‑Oriented Systems

Tencent Cloud Developer

Oct 11, 2019 · Cloud Computing

Large-Scale Distributed Reinforcement Learning Solution Based on TKE

The project replaces cumbersome manual management of thousands of heterogeneous CPU and GPU nodes for large‑scale reinforcement learning with a TKE‑based, containerized actor‑learner architecture that automates batch start/stop, provides elastic autoscaling, fault‑tolerant processes, shared model storage, and CI‑driven image deployment, cutting costs by up to two‑thirds while dramatically speeding experiment cycles.

CI/CDCloud NativeDistributed Training

0 likes · 14 min read

Large-Scale Distributed Reinforcement Learning Solution Based on TKE

DataFunTalk

Sep 30, 2019 · Artificial Intelligence

Reinforcement Learning for Recommender Systems: Challenges, Solutions, and Key Papers

This article reviews recent advances in applying reinforcement learning to recommendation systems, explains the fundamental RL concepts, discusses the specific challenges such as large action spaces, bias, and long‑term reward modeling, and summarizes two influential YouTube papers along with practical insights and future directions.

Off-PolicyUser Modelinglong-term reward

0 likes · 13 min read

Reinforcement Learning for Recommender Systems: Challenges, Solutions, and Key Papers

DataFunTalk

Sep 19, 2019 · Artificial Intelligence

Alibaba Cloud Xiaomai Dialogue System: Architecture, NLU, Dialogue Management, and User Simulator

This article presents Alibaba's Xiaomai intelligent dialogue platform, detailing its general system architecture, three-tier NLU approaches for zero‑, few‑, and many‑shot scenarios, platform‑centric dialogue management with TaskFlow, robustness and continuous learning mechanisms, and a user simulator for large‑scale data generation and dialogue diagnosis.

Natural Language Understandingdialogue systemmeta-learning

0 likes · 13 min read

Alibaba Cloud Xiaomai Dialogue System: Architecture, NLU, Dialogue Management, and User Simulator

DataFunTalk

Sep 18, 2019 · Operations

Understanding Didi's Ride‑Hailing Dispatch Algorithm: Challenges, Models, and Strategies

This article explains why modern ride‑hailing platforms need advanced dispatch algorithms, describes the underlying order‑allocation problem, explores simple and complex matching scenarios, and introduces batch matching, supply‑demand prediction, chain dispatch, and AI‑driven techniques used by Didi to improve efficiency and fairness.

DispatchRide Hailingdynamic VRP

0 likes · 15 min read

Understanding Didi's Ride‑Hailing Dispatch Algorithm: Challenges, Models, and Strategies

Didi Tech

Sep 13, 2019 · Artificial Intelligence

Understanding Didi's Ride‑Hailing Dispatch Algorithms: Challenges and Strategies

Didi’s ride‑hailing dispatch system has progressed from a simple greedy, first‑come‑first‑served matcher to sophisticated batch, chain, and predictive algorithms that use deep‑learning demand forecasts and reinforcement‑learning optimization to assign drivers under complex business rules, boosting response rates and serving over 30 million daily requests.

AIOptimizationRide Hailing

0 likes · 17 min read

Understanding Didi's Ride‑Hailing Dispatch Algorithms: Challenges and Strategies

Alibaba Cloud Developer

Aug 28, 2019 · Artificial Intelligence

Exact‑K Recommendation: Graph Attention Networks and RL from Demonstrations Explained

This article introduces the Exact‑K recommendation problem, highlights its differences from traditional Top‑K approaches, and presents a novel solution combining Graph Attention Networks (GAttN) with Reinforcement Learning from Demonstrations (RLfD), backed by extensive experiments showing superior performance on real-world datasets.

exact-kgraph attention networksmachine learning

0 likes · 14 min read

Exact‑K Recommendation: Graph Attention Networks and RL from Demonstrations Explained

Tencent Cloud Developer

Aug 14, 2019 · Artificial Intelligence

From Atari to AI: The Evolution of Video Games and Artificial Intelligence

From Steve Jobs’s early work at Atari to modern DeepMind breakthroughs, the article traces how video games have grown into a multibillion‑dollar industry that serves as a testbed for AI research, while highlighting current AI techniques for smarter agents, procedural content generation, and the collaborative challenges shaping the future of game development.

Artificial IntelligenceGame DevelopmentMonte Carlo Tree Search

0 likes · 25 min read

From Atari to AI: The Evolution of Video Games and Artificial Intelligence

DataFunTalk

Jul 31, 2019 · Artificial Intelligence

Key Characteristics and Practical Improvements of Recommendation Technologies

This article discusses the fundamental traits of recommendation technologies, compares UserCF and ItemCF models, explains matrix factorization and FM, explores negative sampling, CTR/CVR modeling, ensemble methods, and practical considerations such as reinforcement learning and exploration strategies for improving recommendation performance in real-world systems.

matrix factorizationreinforcement learning

0 likes · 11 min read

Key Characteristics and Practical Improvements of Recommendation Technologies

AntTech

Jul 21, 2019 · Artificial Intelligence

Alipay’s SIGIR 2019 Papers: Reinforcement Learning for User Intent Prediction and Unsupervised QUEST for Complex Question Answering

At SIGIR 2019 in Paris, Alipay presented two AI research papers—one applying reinforcement learning to predict user intent in customer‑service bots and another introducing the unsupervised QUEST method that builds noisy quasi‑knowledge graphs for answering complex multi‑document questions.

AIKnowledge GraphUnsupervised Learning

0 likes · 5 min read

Alipay’s SIGIR 2019 Papers: Reinforcement Learning for User Intent Prediction and Unsupervised QUEST for Complex Question Answering

iQIYI Technical Product Team

Jul 12, 2019 · Artificial Intelligence

Real-Time Evaluation System for Adaptive Bitrate (ABR) Algorithms and Controlled Bitrate Distribution

RESA is a real‑time evaluation platform that continuously tests multiple Adaptive Bitrate (ABR) algorithms on live user traffic, introduces a multi‑user QoE metric derived from viewing behavior, reveals trade‑offs between clarity and bandwidth, and proposes the RL‑based ABSbc algorithm to steer bitrate distribution and balance user experience with network cost.

ABRBandwidth ControlQoE

0 likes · 23 min read

Real-Time Evaluation System for Adaptive Bitrate (ABR) Algorithms and Controlled Bitrate Distribution

Alibaba Cloud Developer

Jun 27, 2019 · Artificial Intelligence

Generating Personalized E‑commerce Review Replies with Product Information

This paper presents a sequence‑to‑sequence model that fuses product‑detail tables with customer comments, using gated multimodal attention, copy mechanisms and reinforcement learning to automatically produce high‑quality, context‑aware replies for e‑commerce platforms, and validates the approach with extensive experiments on a large Taobao dataset.

Sequence-to-Sequencecopy mechanisme‑commerce

0 likes · 21 min read

Generating Personalized E‑commerce Review Replies with Product Information

Ctrip Technology

Jun 19, 2019 · Artificial Intelligence

Applying Reinforcement Learning to Hotel Ranking at Ctrip: Challenges, Solutions, and Preliminary Results

This article examines the limitations of traditional learning‑to‑rank for Ctrip hotel sorting, introduces reinforcement learning as a remedy, outlines three progressive implementation plans (A, B, C) with algorithm choices and engineering trade‑offs, and presents early experimental findings that demonstrate RL's potential to improve conversion rates.

CtripRLRanking

0 likes · 15 min read

Applying Reinforcement Learning to Hotel Ranking at Ctrip: Challenges, Solutions, and Preliminary Results

AntTech

Jun 10, 2019 · Artificial Intelligence

Generative Adversarial User Model for Reinforcement Learning‑Based Recommendation Systems

This article presents a model‑based reinforcement learning framework for recommendation systems that uses a generative adversarial user model to simultaneously learn user behavior dynamics and reward functions, enabling efficient Cascading‑DQN policy learning and achieving superior long‑term user rewards and click‑through rates in experiments.

Artificial IntelligenceCascading DQNGenerative Adversarial Networks

0 likes · 9 min read

Generative Adversarial User Model for Reinforcement Learning‑Based Recommendation Systems

Alibaba Cloud Developer

Apr 1, 2019 · Fundamentals

Must-Read Technical Books Recommended by Alibaba Experts

Alibaba’s senior engineers share their curated list of essential technical books—from software testing and design patterns to AI, machine learning, reinforcement learning, Rust programming, and database architecture—explaining why each title is valuable for developers seeking deeper knowledge and practical insights.

AIDatabase SystemsDesign Patterns

0 likes · 9 min read

Must-Read Technical Books Recommended by Alibaba Experts

DataFunTalk

Mar 8, 2019 · Artificial Intelligence

Alibaba's Intelligent Service Bot (Ali Xiaomì): Platform Overview, Intent Recognition, Machine Reading Comprehension, Multi‑turn Recommendation, and Transfer Learning

The article presents an in‑depth overview of Alibaba's intelligent service bot Ali Xiaomì, covering its platform evolution, core NLP techniques such as intent recognition and machine reading comprehension, multi‑turn recommendation strategies, transfer‑learning approaches across domains and languages, and future technical challenges.

AImachine reading comprehensionnatural language processing

0 likes · 11 min read

Alibaba's Intelligent Service Bot (Ali Xiaomì): Platform Overview, Intent Recognition, Machine Reading Comprehension, Multi‑turn Recommendation, and Transfer Learning

Tencent Cloud Developer

Jan 17, 2019 · Artificial Intelligence

Deep Learning for Big Data Recommendation Systems: Tencent's Industrial Practice

Tencent’s industrial practice shows how a large‑scale offline‑nearline‑online “Shield” recommendation architecture, powered by the DeepR framework built on RCaffe, uses deep semantic embeddings, massive neural networks and reinforcement‑learning decisions to handle billions of daily requests, demonstrating that data richness and engineering capability, not model depth alone, drive performance in big‑data recommendation systems.

Big DataNeural NetworkRCaffe

0 likes · 13 min read

Deep Learning for Big Data Recommendation Systems: Tencent's Industrial Practice

Alibaba Cloud Developer

Jan 15, 2019 · Artificial Intelligence

How Alibaba Engineers Boost SEO with Reinforcement Learning and Attention Models

This article details Alibaba.com engineers' application of reinforcement learning, attention mechanisms, and weakly supervised techniques to extract product summaries, improve content quality, and significantly raise SEO rankings, supported by offline experiments, online A/B testing, and future research directions.

AlibabaSEOText Summarization

0 likes · 16 min read

How Alibaba Engineers Boost SEO with Reinforcement Learning and Attention Models

DataFunTalk

Jan 9, 2019 · Artificial Intelligence

Reinforcement Learning in Natural Language Processing: Concepts, Challenges, and Applications

This article introduces reinforcement learning fundamentals, contrasts it with supervised learning, and explores its challenges and advantages in natural language processing, including applications such as text classification, relation extraction from noisy data, and weakly supervised topic segmentation, while summarizing key insights and experimental results.

Text classificationWeak Supervisionnatural language processing

0 likes · 11 min read

Reinforcement Learning in Natural Language Processing: Concepts, Challenges, and Applications

ThinkingAgent

Nov 22, 2018 · Artificial Intelligence

Understanding Machine Learning Categories: Supervised, Unsupervised, Semi‑Supervised, and Reinforcement

The article explains the four main machine‑learning categories—supervised, unsupervised, semi‑supervised, and reinforcement learning—detailing how each approach handles data labeling, typical algorithms, and common application scenarios.

AI FundamentalsSupervised LearningUnsupervised Learning

0 likes · 2 min read

Understanding Machine Learning Categories: Supervised, Unsupervised, Semi‑Supervised, and Reinforcement

Alibaba Cloud Developer

Nov 20, 2018 · Artificial Intelligence

How Reinforcement Learning Powers Interactive Search in E‑Commerce

This article explains how reinforcement learning can be modeled and deployed to enable intelligent, interactive product search on e‑commerce platforms, detailing problem definition, system architecture, training methodology, online results, and future research directions.

deep learningdialogue systeme-commerce

0 likes · 17 min read

How Reinforcement Learning Powers Interactive Search in E‑Commerce

iQIYI Technical Product Team

Nov 16, 2018 · Artificial Intelligence

How Reinforcement Learning Transforms Adaptive Bitrate Streaming

This article explains the principles of adaptive bitrate streaming, compares traditional ABR algorithms with a reinforcement‑learning‑based approach, describes its system architecture and training process, and presents QoS evaluation results that show RL‑driven streaming can improve video quality and smoothness.

ABR algorithmsAIQoS evaluation

0 likes · 8 min read

How Reinforcement Learning Transforms Adaptive Bitrate Streaming

Alibaba Cloud Developer

Nov 16, 2018 · Artificial Intelligence

How Alibaba’s Search Engine Evolved Over a Decade of Double‑11: From Offline Models to Real‑Time AI

This article traces the ten‑year evolution of Alibaba’s e‑commerce search system, detailing four major stages—from the early Pora streaming engine to dual‑link real‑time architectures, the integration of deep and reinforcement learning, and the shift to large‑scale online deep learning—while highlighting the technical drivers and future AI‑enabled search vision.

e-commercemachine learningonline learning

0 likes · 16 min read

How Alibaba’s Search Engine Evolved Over a Decade of Double‑11: From Offline Models to Real‑Time AI

Meituan Technology Team

Nov 15, 2018 · Artificial Intelligence

Reinforcement Learning for Meituan's "Guess You Like" Recommendation Ranking

Meituan enhanced its homepage “Guess You Like” recommendation slot by modeling user‑item interactions as a Markov Decision Process and applying an improved DDPG reinforcement‑learning agent that adjusts the ranking trade‑off parameter, uses advantage‑based Q decomposition, shares actor‑critic weights, and runs in a real‑time TensorFlow pipeline, delivering consistent lifts in click‑through, dwell time, and depth.

DDPGMDP ModelingTensorFlow

0 likes · 21 min read

Reinforcement Learning for Meituan's "Guess You Like" Recommendation Ranking

Tencent Cloud Developer

Oct 18, 2018 · Artificial Intelligence

10 Machine Learning Algorithms You Should Know to Become a Data Scientist

This article outlines the essential role of a data scientist and introduces ten fundamental machine‑learning algorithms—including PCA/SVD, OLS and polynomial regression, regularized linear models, K‑Means, logistic regression, SVM, feed‑forward, convolutional and recurrent neural networks, CRFs, ensemble trees, and reinforcement‑learning methods—while linking to popular Python libraries and tutorials.

AlgorithmsDecision TreesPCA

0 likes · 10 min read

10 Machine Learning Algorithms You Should Know to Become a Data Scientist

Sohu Tech Products

Oct 10, 2018 · Artificial Intelligence

Optimizing News Recall with DDPG Reinforcement Learning and Transformer Architecture

This article explains how reinforcement learning, specifically the DDPG algorithm combined with Transformer-based networks, is applied to improve large‑scale news recall systems, detailing the business scenario, algorithm selection, model architecture, speed optimizations, training challenges, and observed online performance gains.

AIDDPGOnline Advertising

0 likes · 13 min read

Optimizing News Recall with DDPG Reinforcement Learning and Transformer Architecture

DataFunTalk

Sep 27, 2018 · Artificial Intelligence

Applying Machine Learning in Shumei's Business: Supervised, Unsupervised, and Reinforcement Learning Cases

The article presents a comprehensive overview of how Shumei Technology leverages machine learning—including supervised, unsupervised, and reinforcement learning methods—across its credit scoring, fraud detection, advertising, and audio content moderation services, highlighting practical challenges, model fusion techniques, and future research directions.

model fusionreinforcement learning

0 likes · 12 min read

Applying Machine Learning in Shumei's Business: Supervised, Unsupervised, and Reinforcement Learning Cases