Performance Tuning using Reinforcement Learning
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Updated
Apr 13, 2024 - Jupyter Notebook
Performance Tuning using Reinforcement Learning
Automated gaming using machine learning
This project is based on fine-tuning LLM models (FLAN-T5) for text summarisation task using PEFT approach. All evaluation metrics being computed on ROUGE scoring and LoRA optimisation techniques being used for fine-tuning.
The aim of this repository is the analysis and study of computer intelligence and in-depth learning techniques in the development of intelligent gaming agents.
Short own implementation of the game snake. In this project I'am using the ray library together with ray tune and a custom PPO model.
AI agent learns to walk, run, hop and crawl with out any given data using proximal policy optimisation.
Reinforcement Learning in Super Mario using Pytorch and PPO
Repository for the final project of the "Computational Intelligence" course @ PoliTo, 2022/2023
Snake game environment integrated with OpenAI Gym. Proximal Policy Optimization (PPO) implementation for training. Visualization of training progress and agent performance. Easy to understand code.
🚤🏖️BOATS DO VZHHHHH BBBDROOM, BEEEEP, BEEEP, GNAA, HONK, VZHHHHHHHHHHHHHH🏖️🚤
In this project, I have tried to use DeepRL for optimizing the selection of transactions done by the miner to increase the fee when they execute a block on the chain
A deep reinforcement learning Bot for https://kana.byha.top:444/
Modular Reinforcement Learning in PyTorch.
Concept and development of a walking AT-ST Walker (Starwars) ML-agent.
💡 Grasp - Pick-and-place with a robotic hand 👨🏻💻
Reinforcement Learning based navigation
Developed an highly customizable OpenAI gym environment and trained a stable_baselines3 PPO agent. Used the expert agent for Imitation Learning with DAgger
Multi agent gym environment based on the classic Snake game with implementations of various reinforcement learning algorithms in pytorch
Deep Reinforcement Learning for Trading
Adversarial attacks on Deep Reinforcement Learning (RL)
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