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.

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

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

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

A deep reinforcement learning Bot for https://kana.byha.top:444/

Modular Reinforcement Learning in PyTorch.

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

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

Unleashing the Power of PPO: Mastering Super Mario with Reinforcement Learning. Dive into our journey of training a Proximal Policy Optimization (PPO) agent to conquer the classic NES.

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.

无人机自主溯源甲烷羽流系统/Autonomous UAV Methane Plume Tracing System

Personal project - attempting to train an RL model to trade crypto/other markets

This repository contains the code for a project paper for a Master's module in the field of reinforcement learning. The aim of the project is to explore and implement Proximal Policy Optimization (PPO) agents to learn and play the 7x7 Hex game.

An RL agent that finds an optimal policy of cleaning dirt off a floor with a power washer.

This project integrates MicroK8s (lightweight Kubernetes) with Reinforcement Learning (RL) for adaptive autoscaling in startups, reducing cloud costs by up to 30% compared to traditional solutions (HPA/CA).

Toy model implementing various architectures to teach a generic point of mass to reach a static and/or dynamic target in a 3D space.

Training a Mario reinforcement learning agent using Open AI Gym and Stable Baselines 3 PPO algorithm.