#Proximal-policy-optimization
Showing 15 of 15 repositories tagged #proximal-policy-optimization, ranked by stars
High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly features (PPO, DQN, C51, DDPG, TD3, SAC, PPG)
An Easy-to-use, Scalable and High-performance Agentic RL Framework based on Ray (PPO & DAPO & REINFORCE++ & VLM & TIS & vLLM & Ray & Async RL)
Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学
PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation (ACKTR) and Generative Adversarial Imitation Learning (GAIL).
Minimal implementation of clipped objective Proximal Policy Optimization (PPO) in PyTorch
Proximal Policy Optimization (PPO) algorithm for Super Mario Bros
This repository contains most of pytorch implementation based classic deep reinforcement learning algorithms, including - DQN, DDQN, Dueling Network, DDPG, SAC, A2C, PPO, TRPO. (More algorithms are still in progress)
Deep Reinforcement Learning (PPO) in Autonomous Driving (Carla) [from scratch]
lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
Trading Environment(OpenAI Gym) + PPO(TensorForce)
Human brain cells play Doom (CL1)
PyTorch implementations of algorithms from "Reinforcement Learning: An Introduction by Sutton and Barto", along with various RL research papers.
An implementation of PPO in Pytorch
A PPO agent leveraging reinforcement learning performs Penetration Testing in a simulated computer network environment. The agent is trained to scan for vulnerabilities in the network and exploit them to gain access to various network resources.
This repository contains the lab work for Coursera course on "Generative AI with Large Language Models".