This article provides a concise overview of a ten‑chapter reinforcement‑learning textbook, outlining the progression from basic concepts such as states and rewards to advanced algorithms like policy gradients and actor‑critic methods, and explains how each chapter builds on the previous ones.
This article explains how reinforcement learning (RL) underpins intelligent AI agents, covering the Markov Decision Process fundamentals, key RL components, multi‑hop reasoning on knowledge graphs, and a step‑by‑step LangGraph example that integrates an RL‑driven tutoring policy with Python code.
This article presents the SL‑MGAC framework, a supervised‑learning‑enhanced multi‑group Actor‑Critic algorithm that improves live‑stream insertion decisions in mixed short‑video and live‑stream recommendation systems, achieving higher stability and better long‑term user engagement while satisfying platform constraints, as validated by extensive offline and online experiments.
This article provides a thorough, step‑by‑step explanation of reinforcement‑learning theory—covering policy‑based objectives, value‑function definitions, the derivation of policy gradients, actor‑critic architecture, advantage estimation, importance sampling, GAE, and the PPO algorithm—aimed at readers with little prior RL knowledge.
This talk presents Kuaishou's research on combining reinforcement learning with multi‑task recommendation, detailing a two‑stage constrained actor‑critic method for short‑video ranking, a multi‑task RL framework, experimental results on offline and online systems, and practical Q&A insights.
The presentation introduces a two‑stage constrained actor‑critic algorithm that learns auxiliary policies for interaction signals before optimizing watch‑time under KL constraints, and a reinforcement‑learning multi‑task learning framework that models session‑level dynamics with adaptive multi‑critic weighting, both achieving significant offline and online gains in short‑video recommendation.
This article provides a comprehensive overview of reinforcement learning, covering fundamental concepts, differences from supervised learning, algorithm families, policy gradient methods, practical tricks like baselines and reward‑to‑go, and detailed explanations of TRPO and PPO variants with illustrative diagrams.
The paper models short‑video recommendation as a constrained Markov decision process and introduces a two‑stage constrained actor‑critic algorithm that jointly maximizes watch time while satisfying multiple interaction constraints, demonstrating superior offline and online performance on the KuaiRand dataset and Kuaishou app.
This article introduces the basic concepts of reinforcement learning, derives model‑based and model‑free policy gradient methods—including vanilla policy gradient and Actor‑Critic—explains their mathematical foundations, and demonstrates their use in scene text recognition and image captioning tasks.
This article introduces deep reinforcement learning by explaining its Markov decision process foundations, then categorizes the main algorithm families—value‑based methods like DQN, policy‑based approaches such as PG/DPG/DDPG, and actor‑critic techniques including A3C, PPO, and DDPG—detailing their architectures, training procedures, and key advantages.
The paper proposes an actor‑critic reinforcement‑learning model (ACRL) that leverages PPO and a deep structured semantic model to optimize real‑time bidding strategies for mobile game ads under CPM and budget constraints, addressing long user lifecycles and sparse conversion data while demonstrably improving ROI in both offline simulations and online A/B tests.
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.
