Clean and flexible implementation of PPO (built on top of stable-baselines3)

Repositorio para el contenido relativo al trabajo de fin de máster desarrollado en el Máster de Inteligencia Artificial de la Universidad Internacional de La Rioja (UNIR).

This repository contains my assignment solutions for the Deep Reinforcement Learning course (430.729_003) offered by Seoul National University (Spring 2020).

Training a PPO to balance a pendulum in a fully observable environment.

Reinforcement learning agent for playing Flappy Bird, as part of a university project

A pytorch project to easily run experiments on OpenAI's Procgen Benchmark

Built and trained a model using OpenAI gym, NES emulator to play Super Mario. Optimized the model using preprocessing techniques and vectorization. The algorithm used is PPO (Proximal Policy Optimal) along with Reinforcement Learning.

A demonstration of some prominent reinforcement learning algorithms

Single file implementation of Deep Reinforcement Learning algorithm (PPO) based on LunarLander-v2 environment

Example PPO implementation with ReLAx

Ask About Symptoms is an LLM that has an in-depth understanding of health. The creator of the original version known as DoctorGPT, Siraj Raval, says it works offline, it's cross-platform, & the health data is said to be kept private. We are learning how to build this in our community.

SimplyPPO replicates Proximal-Policy-Optimization with minimum (~250) lines of code in clean, readable PyTorch style, while trying to use as few additional tricks and hyper-parameters as possible (PyBullet benchmarks included).

The CAT Optimal Hybrid Solver is a tool designed to tackle the cross array task (CAT) activity designed to assess algorithmic thinking skills in the context of K-12 education.

PPO implementation for the cable suspended load quadrotor

Train double-jointed arms to reach target locations using Proximal Policy Optimization (PPO) in Pytorch

This project implements an agent for playing the SonicTheHedgehog2 game from a ROM file using the Proximal Policy Optimization (PPO) algorithm from the stablebaselines3 library. The agent is trained to learn the optimal actions to take at each step in the game in order to complete the level and maximize the score.

This repository provides an implementation of Othello game playing agents trained using reinforcement learning techniques.

MarioPPO implementation uses the TensorFlow machine learning platform

Inventory Control with Lateral Transshipment Using Proximal Policy Optimization, DOCS2023

This repository contains a re-implementation of the Proximal Policy Optimization (PPO) algorithm, originally sourced from Stable-Baselines3.