My name is Arseni Perchik and this page presents my GitHub projects in a structured way. The main fields of my work are Multi-agent Systems (MAS), Search AI, RL, ROS, MARL, ML, WEB, IOT. The projects are divided into sections for the sake of convenience.
π¦ π¦ πΉ MAS Algorithms
-
[DCOP CAMS, Max-Sum_MST, DSA_MST, CADSA, DSSA](https://github.com/Arseni1919/dcop_simulator_3)π -
MARL MADDPG π (PPO in MA setting) -
[MARL FedRL](https://github.com/Arseni1919/FedRL_implementation) -
[MARL ae_comm](https://github.com/Arseni1919/Implementation_of_AE_COMM) -
[MAPF CA*, CBS, DSA_MAPF, MGM_MAPF](https://github.com/Arseni1919/MAPF_Simulator)
TODO: MAPPO, QMix, ROMA, COMA, Value Decomposition, MF-Q, MF-AC, MAAC, DBS-DQN, DGN, MASAC, MATD3, QTRAN, MULTI-AGENT AUTOCURRICULA, IQL, TarMAC, SEAC, BiCNet
π¦ RL Algorithms
- Learning Multi-Armed Bandits
- Learning Dynamic Programming (Policy Iteration, Value Iteration)
- Learning Monte-Carlo RL
- Learning TD-Learning
- DQN (variant 2, variant 3)
- REINFORCE (variant 2, variant 3)
- A2C (A3C)
- PPO (variant 2)
- DDPG (variant 2)
- SAC
TODO: I2A, TD3, PPG, HER, POLO, MuZero
π ML and DL Algorithms
TODO: Genetic Algorithms, GANs, Regression, Logistic Regression, K-Nearest Neighbors, Naive Bayes, Support Vector Machines (SVM), Monte Carlo Tree Search (MCTS) (source 1), Decision Tree, Random Forest, AdaBoost, Gradient Boost, CatBoost, XGBoost, LightGBM, Graph NN (source 1 - DGL, source 2)
π Search Algorithms
- RRT Implementation - Generic Version
- A* Implementation - Generic Version
- A* Simulator - Pathfinding
- Learning Topological Sorting
π Research
Simulators
- MAS simulator
-
[DCOP Toy DCOP Max-sum Simulation](https://github.com/Arseni1919/toy_dcop_max_sum_simulation) -
[DCOP Simulator DCOP_MST (version 5)](https://github.com/Arseni1919/dcop_simulator_5) (prev: (version 1), (version 2). (version 3), (version 4)) -
[DCOP Project of Ben Rahmut](https://github.com/Arseni1919/Bens_Project) -
[DCOP DCOP-MDP Simulator](https://github.com/Arseni1919/DCOP_MDP_Model) -
[DCOP Async-DCOP_MST Simulator version 2 (with Ben)](https://github.com/benrachmut/CA_DCOP_MST) (prev: (version 1 - incorrect)) -
[MAPF MAPF simulator (version 2)](https://github.com/Arseni1919/MAPF_Simulator_2) (prev: (version 1)) -
[MAPF Gentleman_Algorithm](https://github.com/Arseni1919/Gentleman_Algorithm_MAPF) -
[MAPF Potential Fields in MAPF](https://github.com/Arseni1919/AvoidCrowdAlgorithm) -
[MAPF & LMAPF Potential Fields in Lifelong MAPF](https://github.com/Arseni1919/PotentialFields_in_Lifelong-MAPF) -
[SACG & LMAPF Corridor-Generating Algorithm](https://github.com/Arseni1919/Corridor_Generating_Algorithm) -
[MAPF & LMAPF Corridor-Generating Algorithm for Multi-Agent Path-Finding Problem](https://github.com/Arseni1919/CGA_MAPF_Algorithm)
π Robots
-
[ROS ROS Package for Hamster Robots](https://github.com/Arseni1919/ROS-package-to-move-robots-with-my-code) -
[Search Voronoi + A* + RRT (robot navigation)](https://github.com/matanSamina/RRT_Project_2021) -
[DCOP Implementation of Max-Sum_MST in ROS Platform](https://github.com/Arseni1919/max_sum_ROS_implementation) -
[DCOP Implementation of CAMS in ROS Platform](https://github.com/Arseni1919/max_sum_cells_ROS)
π΅ Trading
- Current: Stocks Simulator (Streamlit + Matplotlib)
- Learning Advances in Financial ML
- Learning to implement NN on Stocks
- GA in stocks
Previous Projects
- (Trading_model_first_trying), (streamlit app 1), (streamlit app 2), (website - flask), (website - react), (Stocks Gym Env)
Other Projects
ποΈ Skills
- Learning Git and GitHub
- Learning PyTorch
- Learning ROS Essentials
- Learning PL
- Learning SimPy
- Learning Tkinter
- Learning Matplotlib
- Learning Plotly
- Learning Pandas
- Learning Neptune.ai
- Learning Gradio.app
- Learning Pygame 1
- Learning Pygame 2
- Learning SQLite
- Learning Gym (OpenAI)
- Learning PettingZoo Environments
- Learning Docker
- Learning PyTorch Geometric
- Learning Electron
- Learning C++
- Learning Streamlit
- Learning FFT
- Learning GPT with Karpathy
- Learning Pogema
π Python
- Learning Python
- Learning Seed in Python
- Learning Python Tricks
- Learning cProfile
- Learning Threading in Python
- Learning to save files in Python
- Learning Datetime
- Learning Async IO
- Learning Python Decorators
- Learning f-strings
- Learning Dataclasses
- Learning Type-Checking in Python
- Learning Magic Methods in Python
- Learning Collections
- Learning @property
- Learning Regexes
- Learning Matplotlib Animation
π₯οΈ WEB
- Learning HTML
- Learning CSS
- Learning CSS Grid
- Learning Flexbox
- Learning CSS Animation
- Learning Flask
- Learning React
- Learning Django
- Learning AJAX
- Learning Async JS and Fetch API
- Learning Bootstrap
- Learning To Deploy Flask App To Heroku
- Learning To Deploy Flask App With DB To Heroku
- Learning Dash (source 1)
- Learning React + Flask + MongpDB
- Learning MongoDB
π University Projects
Studying
- DL Course 1: Image Classification with HOG and SVM Techniques
- DL Course 2: TensorFlow and Keras
- Air Hockey Simulation - RL - Tabular Q-learning and Sarsa
- Fuzzy Logic Presentation
- DRL Course 1: MDP, Value Iteration, Policy Iteration
- DRL Course 2: SARSA, Q-learning
- DRL Course 3: DQN, PyTorch
- Greedy Algorithm, Construction Heuristic, Simulated Annealing, Local Search, Genetic Algorithm
- CSP problems
- Stanford cs321n
- Multi-variate Statistics Course (2022)
Teaching
WEB Course
- Lectures and Assignments
- Flask Begginig
- Jinja2
- Flask Requests
- MySQL
- Blueprints
- Learning Fetch, Requests,
asyncioin Flask and Plotting Graphs in Flask - REST API
- Bootstrap
Lectures
- (former repo of the course)
- WEB Course: Flask Skeleton Project (By Barak Pinchovski)
- WEB Course: Lectures - 2021 A
- WEB Course: Lectures - 2021 B
- WEB Course: Lectures - 2022 A - group 1
- WEB Course: Lectures - 2022 A - group 2
- WEB Course: Lectures - 2022 B
- WEB Course: Lectures - 2023 A - group 1
- WEB Course: Lectures - 2023 A - group 2
- WEB Course: Lectures - 2023 B
- WEB Course: Lectures - 2024 A - group 1
- WEB Course: Lectures - 2024 A - group 2
- WEB Course: Lectures - 2024 B
IOT Course
π Competitions
π Others
- My Portfolio
- Porftrofilo Template
- Forked: Smart Home Simulator - FinalProject
- Forked: Smart Home Simulator - FinalProjectWrapping
Help-Links and Markdown
Usefull Links π Mastering Markdown link Emoji Cheat Sheet link Theme of the Page link GitHub Pages Site link GitHub Pages Docs link Budges in GitHub README link
π» RL and MARL Environments (Existing In The World)
- OpenAI Gym / PettingZoo / MiniGrid
- VMAS - VectorizedMultiAgentSimulator (yes)
- CityFlow
- MiniHack
- Derkβs Gym
- RWARE
- Neural MMO 2.0 (Prev: 1.0)
- Flatland
- highway-env
- Deep RTS
- Gym-ΞΌRTS (pronounced βgym-micro-RTSβ)
- Nocturne
- CyberBattleSim
- AI Economist - An Economic Simulation Framework
- Neural MMO
- Bigpig4396/Multi-Agent-Reinforcement-Learning-Environment
- PyBullet (no)
- Pogema
- Duckietown Gym Env
- Automatic Parallel Parking: Path Planning, Path Tracking & Control
- PythonRobotics
- Jumanji
- PGX
- FinRL: Financial Reinforcement Learning
- RLCard: A Toolkit for Reinforcement Learning in Card Games