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.

控制算法，状态、输出反馈控制。ADRC自抗扰控制，抗积分饱和PID控制，增量式PID控制，模糊FuzzyPID控制，线性二次型调节器LQR控制，线性二次型积分器LQI控制，迭代iLQR控制，模型预测MPC控制，AI智能控制，启发算法控制，强化学习SAC、PPO控制，无人机、机器人、小车轨迹跟踪控制

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

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"

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

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.

Hybrid Action PPO in stable-baselines3

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

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

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

GAIL learning to imitate PPO playing CartPole.

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.