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AI Algorithm Path
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AI Algorithm Path

A public account focused on deep learning, computer vision, and autonomous driving perception algorithms, covering visual CV, neural networks, pattern recognition, related hardware and software configurations, and open-source projects.

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Recent Articles

Latest from AI Algorithm Path

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AI Algorithm Path
AI Algorithm Path
Jun 3, 2025 · Artificial Intelligence

Inside Tencent’s HunyuanVideo-Avatar: How Open‑Source AI Generates Digital Human Videos

Tencent’s HunyuanVideo-Avatar converts a static portrait and an audio clip into a lip‑synced, expressive video using a multimodal diffusion Transformer, offering open‑source weights, detailed module designs, hardware requirements, code examples, and a candid assessment of its strengths and current limitations.

AI video generationCUDAHunyuanVideo-Avatar
0 likes · 8 min read
Inside Tencent’s HunyuanVideo-Avatar: How Open‑Source AI Generates Digital Human Videos
AI Algorithm Path
AI Algorithm Path
May 27, 2025 · Artificial Intelligence

Reinforcement Learning Tutorial 8: Building State Feature Representations for Objective Optimization

This tutorial explains how to construct state feature vectors for reinforcement‑learning value‑function approximation, covering linear, polynomial, Fourier, and radial‑basis representations, as well as state aggregation techniques such as coarse coding and tile coding, and discusses non‑parametric approaches like kernel methods.

feature engineeringfourier basisfunction approximation
0 likes · 16 min read
Reinforcement Learning Tutorial 8: Building State Feature Representations for Objective Optimization
AI Algorithm Path
AI Algorithm Path
May 25, 2025 · Artificial Intelligence

Reinforcement Learning Tutorial 7: Introducing Value Function Approximation Methods

This article explains why tabular reinforcement‑learning methods scale poorly, introduces supervised‑learning‑based value‑function approximation using a parameterized vector w, discusses loss design, stochastic‑gradient updates, bootstrapping, semi‑gradient techniques, and linear function approximation, and summarizes practical implications.

gradient Monte Carlolinear function approximationreinforcement learning
0 likes · 13 min read
Reinforcement Learning Tutorial 7: Introducing Value Function Approximation Methods
AI Algorithm Path
AI Algorithm Path
May 24, 2025 · Artificial Intelligence

Claude 4 Unveiled: What the New AI Model Means for Coding, Safety, and Pricing

Claude 4 introduces two upgraded models—Opus 4, touted as the world’s best coding model, and Sonnet 4 with stronger reasoning—along with new tool‑use capabilities, benchmark wins, a controversial safety test showing opportunistic extortion, and detailed pricing and availability in the Cursor IDE.

AI modelAnthropicClaude 4
0 likes · 10 min read
Claude 4 Unveiled: What the New AI Model Means for Coding, Safety, and Pricing
AI Algorithm Path
AI Algorithm Path
May 24, 2025 · Artificial Intelligence

How N-step Temporal-Difference Methods Extend TD Learning in Reinforcement AI

This tutorial explains how n-step temporal‑difference (TD) algorithms generalize the one‑step TD and Monte‑Carlo methods, presents the n‑step return update rule, walks through a three‑step TD example, shows how Sarsa and Q‑learning can be extended, and discusses how to choose the optimal n value for a given problem.

Monte Carloalgorithm analysisn-step td
0 likes · 9 min read
How N-step Temporal-Difference Methods Extend TD Learning in Reinforcement AI
AI Algorithm Path
AI Algorithm Path
May 23, 2025 · Artificial Intelligence

Understanding Temporal‑Difference Algorithms in Reinforcement Learning

This tutorial explains temporal‑difference (TD) learning, compares it with dynamic programming and Monte‑Carlo methods, walks through concrete soccer‑match examples, shows one‑step TD versus constant‑α Monte‑Carlo updates, discusses convergence, bias, and introduces popular TD variants such as Sarsa, Q‑learning, Expected Sarsa and double learning.

Monte CarloTD learningmaximization bias
0 likes · 18 min read
Understanding Temporal‑Difference Algorithms in Reinforcement Learning
AI Algorithm Path
AI Algorithm Path
May 22, 2025 · Artificial Intelligence

Monte Carlo Policy Improvement in RL: Epsilon‑Greedy, On‑Policy vs Off‑Policy, and Incremental Updates

This tutorial explains how Monte Carlo methods are enhanced in reinforcement learning through epsilon‑greedy and epsilon‑soft policies, Monte Carlo control, a Blackjack Q‑function example, the distinction between on‑policy and off‑policy learning, importance sampling, and efficient incremental update techniques.

Epsilon-GreedyImportance SamplingIncremental Update
0 likes · 14 min read
Monte Carlo Policy Improvement in RL: Epsilon‑Greedy, On‑Policy vs Off‑Policy, and Incremental Updates
AI Algorithm Path
AI Algorithm Path
May 19, 2025 · Artificial Intelligence

Understanding Policy Evaluation and Improvement in Reinforcement Learning

This article explains how to solve Bellman equations, use iterative policy‑evaluation methods, apply the policy‑improvement theorem, and combine both steps in policy iteration, value iteration, and asynchronous variants, illustrated with a 5‑state example and a 4×4 gridworld.

Bellman Equationgeneralized policy iterationgridworld
0 likes · 15 min read
Understanding Policy Evaluation and Improvement in Reinforcement Learning
AI Algorithm Path
AI Algorithm Path
May 18, 2025 · Artificial Intelligence

Reinforcement Learning Tutorial Part 1: Core Concepts Explained

This article introduces the fundamental concepts of reinforcement learning, covering the agent‑environment interaction, key terminology, reward structures, task types, policies, value functions, the Bellman equations, and how optimal strategies are derived and approximated in practice.

Bellman EquationMarkov Decision ProcessOptimal Policy
0 likes · 13 min read
Reinforcement Learning Tutorial Part 1: Core Concepts Explained