Hello! Iโm Arseniy Pertzovsky, and welcome to my GitHub page, where you can explore my projects organized for your convenience. My primary areas of expertise include Multi-Agent Systems (MAS), Search and Planning in AI, Reinforcement Learning (RL), Multi-Agent Reinforcement Learning (MARL), Robot Operating System (ROS), Machine Learning (ML), Web Development, and the Internet of Things (IoT). Each project is categorized into sections to enhance your browsing experience. Thank you for visiting!
๐ฆ ๐ฆ ๐น MAS Algorithms
-
[DCOP CAMS, Max-Sum_MST, DSA_MST, CADSA, DSSA](https://github.com/Arseni1919/dcop_simulator_3)๐ -
MARL MADDPG ๐ (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: IQL, VDN, QMix, QPlex, MAPPO, IPPO, Belief.-PPO, MASAC, ISAC, MADDPG, IDDPG, SHAQ, DGN, IAC, ROMA, PRIMAL
๐ฆ RL Algorithms
- Template for PL Project
- Gather Game
- Learning Multi-Armed Bandits
- Learning Dynamic Programming (Policy Iteration, Value Iteration)
- Learning Monte-Carlo RL
- Learning TD-Learning
- Learning DRL: DQN, Double-DQN, REINFORCE, Actor-Critic, A2C, DDPG, TD3, PPO, SAC
Previous Projects
DQN (variant 2, variant 3) | REINFORCE (variant 2, variant 3) | A2C (A3C) | PPO (variant 2) | DDPG (variant 2) | SAC
๐ 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)
๐ Search Algorithms
- RRT Implementation - Generic Version
- A* Implementation - Generic Version
- A* Simulator - Pathfinding
- Learning Topological Sorting
- MAPF-LNS2 (+SIPPS), PrP (+SIPPS), SIPPS Implementations in Python
- LaCAM (+PIBT), LaCAM* (+PIBT), PIBT Implementations in Python
๐ 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)) - Gentleman_Algorithm (MAPF)
-
[Vanila APFs for MAPF Potential Fields in MAPF](https://github.com/Arseni1919/AvoidCrowdAlgorithm) -
[APFs for MAPF & LMAPF Artificial Potential Fields in MAPF and Lifelong MAPF (version 2)](https://github.com/Arseni1919/APFs_for_MAPF_Implementation_v2) (prev: version 1) -
[CGA, SACG, CGA-LMAPF Corridor-Generating Algorithm and Single-Agent Corridor-Generating problem](https://github.com/Arseni1919/Corridor_Generating_Algorithm) -
[CGA-MAPF Corridor-Generating Algorithm for MAPF (version 2)](https://github.com/Arseni1919/CGA_MAPF_Implementation_v2)(prev: version 1)
๐ 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)
๐ต Trading
- Current: ML For Trading - Drafts
- Stocks Simulator (Streamlit + Matplotlib)
- Learning to implement NN on Stocks
- GA in stocks
- Learning from โAdvances in Financial MLโ book
- Learning from โML for Tradingโ book
Previous Projects
- (Trading_model_first_trying), (streamlit app 1), (streamlit app 2), (website - flask), (website - react), (Stocks Gym Env)
Other Projects
๐๏ธ 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
๐ 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
๐ฅ๏ธ 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
๐ 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
- WEB Course: Lectures - 2025 A - group 1
- WEB Course: Lectures - 2025 A - group 2
IOT Course
๐ Competitions
๐ Others
- My Portfolio
- Porftrofilo Template
- Forked: Smart Home Simulator - FinalProject
- Forked: Smart Home Simulator - FinalProjectWrapping
Help-Links and Markdown
Usefull Links ๐ 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
๐ป RL and MARL Environments (Existing In The World)
- Toy envs - OpenAI Gym
- SOTA RL envs - OpenAI Gym, minigrid
- SOTA MARL envs - VMAS, MAgent2, SMAC-v1, Derkโs Gym
- SOTA RL and MARL envs - Unity ML-Agents Toolkit
Single-Agent RL
- OpenAI Gym
- minigrid
- MiniHack
- CyberBattleSim
- PyBullet
- Duckietown Gym Env
- Automatic Parallel Parking: Path Planning, Path Tracking & Control
- Jumanji
- FinRL: Financial Reinforcement Learning
- Aerial Gym Simulator
- OGBench: Benchmarking Offline Goal-Conditioned RL
- Kinetix
Multi-Agent RL
- MAgent2
- Derkโs Gym
- VMAS
- PettingZoo
- CityFlow
- RWARE
- Neural MMO 2.0 (Prev: 1.0)
- Flatland
- highway-env
- Nocturne - partially observed, driving simulator
- AI Economist - An Economic Simulation Framework (deprecated)
- Bigpig4396/Multi-Agent-Reinforcement-Learning-Environment
- Pogema
- JaxMARL
- Airlift Challenge v2.0
- overcooked_ai
- gym-cooking
- Google Research Football
- IMP-MARL: a Suite of Environments for Large-scale Infrastructure Management Planning via MARL
- LBF
- MATE: the Multi-Agent Tracking Environment
- Melting Pot
- Multi-Car Racing Gym Environment
- SMAC-v1 - StarCraft Multi-Agent Challenge
- SMAC-v2
Mixed
- OpenAI Gym / PettingZoo / MiniGrid
- Unity ML-Agents Toolkit
- Deep RTS
- Gym-ฮผRTS (pronounced โgym-micro-RTSโ)
- PGX
- RLCard: A Toolkit for Reinforcement Learning in Card Games
- Griddly
- PufferLib