Young_Painter_L's repositories
Awesome-LLM-Robotics
A comprehensive list of papers using large language/multi-modal models for Robotics/RL, including papers, codes, and related websites
awesome-open-data-annotation
Open Source Data Annotation & Labelling Tools
basic-algo-lecture
바킹독의 실전 알고리즘 강의 자료
coyo-dataset
COYO-700M: Large-scale Image-Text Dataset
CrazyRL
Environments and code for doing RL with Crazyflies
driver-dojo
A benchmark towards generalizable reinforcement learning for autonomous driving.
envpool
C++-based high-performance parallel environment execution engine (vectorized env) for general RL environments.
Eureka
Official Repository for "Eureka: Human-Level Reward Design via Coding Large Language Models"
evosax
Evolution Strategies in JAX 🦎
faster-fifo
Faster alternative to Python's multiprocessing.Queue (IPC FIFO queue)
generative_agents
Generative Agents: Interactive Simulacra of Human Behavior
go-explore
Code for Go-Explore: a New Approach for Hard-Exploration Problems
irm
Intrinsic Reward Matching (IRM) implementation (from Adeniji and Xie et al 2022)
jaxton
100 exercises to learn JAX
KoreanLM
한국어 언어모델 오픈소스
LightZero
LightZero: A lightweight and efficient MCTS/AlphaZero/MuZero algorithm toolkit.
mealpy
A Collection Of The State-of-the-art Metaheuristic Algorithms In Python (Metaheuristic/Optimizer/Nature-inspired/Biology)
MetaGym
Collection of Reinforcement Learning / Meta Reinforcement Learning Environments.
MineLand
Simulating Large-Scale Multi-Agent Interactions with Limited Multimodal Senses and Physical Needs
PPOxFamily
PPO x Family DRL Tutorial Course（决策智能入门级公开课：8节课帮你盘清算法理论，理顺代码逻辑，玩转决策AI应用实践 ）
rl-exploration-baselines
RLeXplore provides stable baselines of exploration methods in reinforcement learning, such as intrinsic curiosity module (ICM), random network distillation (RND) and rewarding impact-driven exploration (RIDE).
ScoreDiffusionModel
The Pytorch Tutorial of Score-based and Diffusion Model
Semantic-Retrieval-Models
A curated list of awesome papers for Semantic Retrieval (TOIS Accepted: Semantic Models for the First-stage Retrieval: A Comprehensive Review).
system-design-101
Explain complex systems using visuals and simple terms. Help you prepare for system design interviews.
TeachYourselfCS-KR
컴퓨터 과학 스스로 학습하기 https://teachyourselfcs.com
TimeChamber
A Massively Parallel Large Scale Self-Play Framework
tsfresh
Automatic extraction of relevant features from time series: