Reinforcement learning tutorials

This repository hosts a customized PPO based agent for Carla. The goal of this project is to make it easier to interact with and experiment in Carla with reinforcement learning based agents -- this, by wrapping Carla in a gym like environment that can handle custom reward functions, custom debug output, etc.

Adversarial attacks on Deep Reinforcement Learning (RL)

Deep Reinforcement Learning for Trading

Multi agent gym environment based on the classic Snake game with implementations of various reinforcement learning algorithms in pytorch

💡 Grasp - Pick-and-place with a robotic hand 👨🏻‍💻

Developed a highly customizable OpenAI gym environment and trained a stable_baselines3 PPO agent. Used the expert agent for Imitation Learning with DAgger

Reinforcement Learning based navigation

🚤🏖️BOATS DO VZHHHHH BBBDROOM, BEEEEP, BEEEP, GNAA, HONK, VZHHHHHHHHHHHHHH🏖️🚤

Financial trading strategies using deep reinforcement learning (DRL). It offers a frameworks for quantitative finance, enabling practitioners to create, test, and implement investments strategies.

Concept and development of a walking AT-ST Walker (Starwars) ML-agent.

Modular Reinforcement Learning in PyTorch.

AI agent learns to walk, run, hop and crawl with out any given data using proximal policy optimisation.

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/

Repository for the final project of the "Computational Intelligence" course @ PoliTo, 2022/2023

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.

Reinforcement Learning in Super Mario using Pytorch and PPO

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.

State Representations as Incentives for Reinforcement Learning Agents: A Sim2Real Analysis on Robotic Grasping