Multi-Agent Pickup and Delivery implementation
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Updated
May 8, 2025 - Python
Multi-Agent Pickup and Delivery implementation
Generalized voronoi diagram for python
🎮 An artificial intelligence game to demonstrate the A* pathfinding. The enemy will try to get close to the agent and make it stuck between walls(用于演示A*寻路算法的人工智能游戏,敌人会追踪宝藏并尝试将其困在围墙之间)
Optimal path planning using dynamic programming
A simulation of path planning using Genetic Algorithm for my CSE474 Project
Real time drone AI for safe and adaptive flight that separates drift from hallucination. Multi agent reinforcement learning with Proximal Policy Optimization ("PPO") for path finding and coordination, served on a FastAPI backend with weight loading at startup, test isolation, and Docker deployment to bridge research into production
A* Algorithm for robot path planning using a simple Euclidean heuristic function.
Simple A* turtlebot3 path planner
PCE developed on top of OpenDaylight for constraint based path calculation
🕸️ Crawl any website into a queryable navigation + content graph and find the shortest click-path to any action — deterministic, no-LLM browser automation
Research Project in A3C reinforcement learning algorithm used for path finding mobile robots
This is my project for CS106 homework - use uninformed and informed search to upgrade auto mode for Sokoban game
⚡ AiLab-workbench — an all-in-one 🧠 AI & data science lab repository 🚀 with Python implementations of path-finding algorithms (BFS, DFS, UCS) 🧭, data processing 📊, and algorithmic problem-solving 🧮. Includes lab assignments 📚, sample datasets 🗂️, and step-by-step scripts 📝 for hands-on learning 💡.
This repository is a demonstration of the classic path-finding algorithm D* in partially known or dynamically changing environments. http://web.mit.edu/16.412j/www/html/papers/original_dstar_icra94.pdf
Academic benchmark of search algorithms on 2D grids. Compares A*, BFS, DFS, Dijkstra and Bidirectional Search with Manhattan, Euclidean, Chebyshev and Octile heuristics using search-library.
Explore a map to find an optimal path from start to goal using classical search-based methods
Code for ICML 2026 paper experiments studying how deep sequence models tend to memorize through a learned geometric structure.
Visualization for the following algorithms: A* Search, Bredth First Search, Depth First Search, and Greedy-Best First Search. In addition to Recursive and DFS maze generation.
MAPF: CBS optimal solver + MAPPO with transformer communication, CBS-bootstrapped curriculum, lifelong goals — pure PyTorch, Mac-native
Rogue-like AI agent for an adaptation of DreamQuest game.
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