Don't miss any moment of global AI innovation
Daily three-minute AI industry trends
AI industry milestones
AI monetization case sharing
AI image creation monetization cases
AI video creation monetization cases
AI audio creation monetization cases
AI content writing monetization cases
Free sharing of the latest AI tutorials
Shows total visits ranking of AI websites
Track fastest growing AI websites by traffic
Focus on AI websites with significant traffic drops
Shows weekly visits ranking of AI websites
AI websites most popular with US users
AI websites most popular with Chinese users
AI websites most popular with Indian users
AI websites most popular with Brazilian users
Total visits ranking of AI image generation websites
Total visits ranking of AI personal assistant websites
Total visits ranking of AI character generation websites
Total visits ranking of AI video generation websites
GitHub popular AI projects by total stars
GitHub popular AI projects by growth rate
GitHub popular AI developer ranking
GitHub popular AI organization ranking
GitHub popular deepseek open source projects
GitHub popular TTS open source projects
GitHub popular LLM open source projects
GitHub popular ChatGPT open source projects
Overview of GitHub popular AI open source projects
Engineering LaCAM*: Towards Real-Time, Large-Scale, and Near-Optimal Multi-Agent Pathfinding (AAMAS-24)
Continuous CBS - a modification of conflict based search algorithm, that allows to perform actions (move, wait) of arbitrary duration. Timeline is not discretized, i.e. is continuous.
POGEMA stands for Partially-Observable Grid Environment for Multiple Agents. This is a grid-based environment that was specifically designed to be flexible, tunable and scalable. It can be tailored to a variety of PO-MAPF settings.
This is a multi-agent path planning(also known as Multi-Agent Path Finding, MAPF) algorithm package for ROS
Algorithm for prioritized multi-agent path finding (MAPF) in grid-worlds. Moves into arbitrary directions are allowed (each agent is allowed to follow any-angle path on the grid). Timeline is continuous, i.e. action durations are not explicitly discretized into timesteps. Different agents' size and moving speed are supported. Planning is carried out in (x, y, \theta) configuration space, i.e. agents' orientation are taken into account.
This repository contains MAPF-GPT, a deep learning-based model for solving MAPF problems. Trained with imitation learning on trajectories produced by LaCAM, it generates actions under partial observability without heuristics or agent communication. MAPF-GPT excels on unseen instances and outperforms state-of-the-art solvers.
LaCAM: Search-Based Algorithm for Quick Multi-Agent Pathfinding (AAAI-23)
JAX-based implementation for multi-agent path planning (MAPP) in continuous spaces.
[IROS 2024] EPH: Ensembling Prioritized Hybrid Policies for Multi-agent Pathfinding
[AAAI-2024] Follower: This study addresses the challenging problem of decentralized lifelong multi-agent pathfinding. The proposed Follower approach utilizes a combination of a planning algorithm for constructing a long-term plan and reinforcement learning for resolving local conflicts.
"When to Switch" Implementation: Addressing the PO-MAPF challenge with RePlan & EPOM policies. This repo includes search-based re-planning, reinforcement learning techniques, and three mixed policies for pathfinding in partially observable multi-agent environments. ???