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.

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.

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.

ReinforceUI-Studio. A Python-based application 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

A Torch Based RL Framework for Rapid Prototyping of Research Papers

Implementation of PPO Lagrangian in PyTorch

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

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

PyTorch implementation of GAIL and PPO reinforcement learning algorithms

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.

DRL-Base-EMS for HEVs

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

Hybrid Action PPO in stable-baselines3

Simple and Modular implementation of Proximal Policy Optimization (PPO) in PyTorch

GAIL learning to imitate PPO playing CartPole.

Vision based RL agent to control a UR5 arm in Human-Robot collaborative environments

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.