Mykhaïlo Lytvynenko's starred repositories
DRL-Pytorch
Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms (Q-learning, Duel DDQN, PER, C51, Noisy DQN, PPO, DDPG, TD3, SAC, ASL)
LLM-Assisted-Light
This repository contains the code for the paper "LLM-Assisted Light: Leveraging Large Language Model Capabilities for Human-Mimetic Traffic Signal Control in Complex Urban Environments".
bootstrap_dqn
Implementation of Bootstrap DQN and Randomized Prior Functions on ALE
MARL-Algorithms
Implementations of IQL, QMIX, VDN, COMA, QTRAN, MAVEN, CommNet, DyMA-CL, and G2ANet on SMAC, the decentralised micromanagement scenario of StarCraft II
bootsrapped-dqn
This is pytorch implmentation project of Bootsrapped DQN
LLMsPracticalGuide
A curated list of practical guide resources of LLMs (LLMs Tree, Examples, Papers)
LLM-RL-Papers
Monitoring recent cross-research on LLM & RL on arXiv for control. If there are good papers, PRs are welcome.
LLM-Prompt-Library
Advanced Code and Text Manipulation Prompts for Various LLMs. Suitable for Siri, GPT-4o, Claude, Llama3, Gemini, and other high-performance open-source LLMs.
langchain-tutorials
Overview and tutorial of the LangChain Library
prompt-engineering
Tips and tricks for working with Large Language Models like OpenAI's GPT-4.
Awesome-LLMOps
An awesome & curated list of best LLMOps tools for developers
nui_in_madrl
Negative Update Intervals in Multi-Agent Deep Reinforcement Learning
generative-ai
Sample code and notebooks for Generative AI on Google Cloud, with Gemini on Vertex AI
training-data-analyst
Labs and demos for courses for GCP Training (http://cloud.google.com/training).
Machine-Learning-with-Python
Practice and tutorial-style notebooks covering wide variety of machine learning techniques
pytorch_seed_rl
A PyTorch implementation of SEED, originally created by Google Research for TensorFlow 2.
ConvLSTM_pytorch
Implementation of Convolutional LSTM in PyTorch.
Spiking-Neural-Network-SNN-with-PyTorch-where-Backpropagation-engenders-STDP
What about coding a Spiking Neural Network using an automatic differentiation framework? In SNNs, there is a time axis and the neural network sees data throughout time, and activation functions are instead spikes that are raised past a certain pre-activation threshold. Pre-activation values constantly fades if neurons aren't excited enough.
CommNet-Reproduced-for-Levers-Task
A pytorch implementation of commNet on the levers task from "Learning Multiagent Communication with Backpropagation" paper. Reproduced from https://github.com/facebookarchive/CommNet