Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch

Concise pytorch implements of DRL algorithms, including REINFORCE, A2C, DQN, PPO(discrete and continuous), DDPG, TD3, SAC.

Proximal Policy Optimization (PPO) algorithm using PyTorch to train an agent for a rocket landing task in a custom environment

PyTorch implementation of some reinforcement learning algorithms: A2C, PPO, Behavioral Cloning from Observation (BCO), GAIL.

Deep Reinforcement Learning for mobile robot navigation in IR-SIM simulation. Using DRL (SAC, TD3, PPO, DDPG) neural networks, a robot learns to navigate to a random goal point in a simulated environment while avoiding obstacles.

A Torch Based RL Framework for Rapid Prototyping of Research Papers

ReinforceUI-Studio. A Python-based application with a graphical user interface designed to simplify the configuration and monitoring of RL training processes. Supporting MuJoCo, OpenAI Gymnasium, and DeepMind Control Suite. Algorithms included: CTD4, DDPG, DQN, PPO, SAC, TD3, TQC

Implementation of PPO Lagrangian in PyTorch

Multi agent PPO implementation in Pytorch for Unity ML Agents environments.

PyTorch implementation of GAIL and PPO reinforcement learning algorithms

Solving pursuit-evasion problems on graphs using Reinfocement Learning and GNNs

Implementation of the IEEE WCNC 2025 'Worst-Case MSE Minimization for RIS-Assisted mmWave MU-MISO Systems With Hardware Impairments and Imperfect CSI' paper

TradeWhisperer is a sophisticated cryptocurrency trading bot that leverages advanced Reinforcement Learning techniques, specifically the Proximal Policy Optimization (PPO) algorithm, to navigate the complex world of crypto markets. Built with a focus on adaptability and risk management, this bot combines technical analysis with machine learning.

Positioning a building mass on topography while minimizing the required cut and fill excavation volume using actor critic methods.

Minimum viable reinforcement learning algorithms for your educational convenience.

This is the Tic-Tac-Toe game made with Python using the PyGame library and the Gym library to implement the AI with Reinforcement Learning

Reinforcement learning (PPO) plays Mario.

implementation of reinforcement learning algorithm that is easy to read and understand

Repository with all source files relating to the 6CCE3EEP Final Year Project titled "Self Parking with Reinforcement Learning." The project was implemented using Python, and used PyGame, OpenAI Gym, and the Stable Baselines-3 libraries in order to implement a Proximal Policy Optimisation (PPO) algorithm.

This repository contains a project that leverages reinforcement learning to make a humanoid robot walk in a PyBullet simulation. It uses a custom Gym environment, a Proximal Policy Optimization (PPO) agent, and a provided URDF file for the robot model. The training process prints rewards per generation and visualizes the robot's behavior.