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.

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

A Torch Based RL Framework for Rapid Prototyping of Research Papers

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

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

DRL-Base-EMS for HEVs

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

Worst-case MSE Minimization for RIS-assisted mmWave MU-MISO Systems with Hardware Impairments and CSI Imperfection

GAIL learning to imitate PPO playing CartPole.

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.

Reinforcement learning (PPO) plays Mario.

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

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.

An adaption of the Flatland environment for TorchRL.

HAMMER: Multi-Level Coordination of Reinforcement Learning Agents via Learned Messaging (Paper: https://ala2021.vub.ac.be/papers/ALA2021_paper_35.pdf)