ppo

39 Projekte teilen dieses GitHub-Topic

Linien verbinden Mitglieder, die messbar miteinander verwandt sind. Die Punktgröße spiegelt die Sterne wider.

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强化学习中文教程（蘑菇书🍄），在线阅读地址：https://datawhalechina.github.io/easy-rl/

High-quality single file implementation of Deep Reinforcement Learning algorithms with research-friendly…

Reinforcement-learning-with-tensorflow

Simple Reinforcement learning tutorials, 莫烦Python 中文AI教学

deep-reinforcement-learning

Repo for the Deep Reinforcement Learning Nanodegree program

Reinforcement-Learning

Learn Deep Reinforcement Learning in 60 days! Lectures & Code in Python. Reinforcement Learning + Deep…

Deep-reinforcement-learning-with-pytorch

PyTorch implementation of DQN, AC, ACER, A2C, A3C, PG, DDPG, TRPO, PPO, SAC, TD3 and ....

Massively Parallel Deep Reinforcement Learning. 🔥

pytorch-a2c-ppo-acktr-gail

PyTorch implementation of Advantage Actor Critic (A2C), Proximal Policy Optimization (PPO), Scalable…

Deep_reinforcement_learning_Course

Implementations from the free course Deep Reinforcement Learning with Tensorflow and PyTorch

Clean, Robust, and Unified PyTorch implementation of popular Deep Reinforcement Learning (DRL) algorithms…

Implementations of basic RL algorithms with minimal lines of codes! (pytorch based)

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

Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep…

Super-mario-bros-PPO-pytorch

Proximal Policy Optimization (PPO) algorithm for Super Mario Bros

PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and…

PPO-for-Beginners

A simple and well styled PPO implementation. Based on my Medium series:…

LearningHumanoidWalking

Training a humanoid robot for locomotion using Reinforcement Learning

Really Fast End-to-End Jax RL Implementations

DeepRL-Tutorials

Contains high quality implementations of Deep Reinforcement Learning algorithms written in PyTorch

XuanCe: A Comprehensive and Unified Deep Reinforcement Learning Library

Hands-On-Reinforcement-Learning-With-Python

Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

autonomous-learning-library

A PyTorch library for building deep reinforcement learning agents.

PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and…

DeepRL-TensorFlow2

🐋 Simple implementations of various popular Deep Reinforcement Learning algorithms using TensorFlow2

RLAnything (ICML 2026) & AutoTool (ICML 2026), DemyAgent: Open-Source RL for LLMs and Agentic Scenarios

Awesome-RL-for-Video-Generation

A curated list of papers on reinforcement learning for video generation

Autonomous-Driving-in-Carla-using-Deep-Reinforcement-Learning

Deep Reinforcement Learning (PPO) in Autonomous Driving (Carla) [from scratch]

Deep-Reinforcement-Learning-With-Python

Master classic RL, deep RL, distributional RL, inverse RL, and more using OpenAI Gym and TensorFlow with…

Long-Term Evolution Project of Reinforcement Learning

rl-collision-avoidance

Implementation of the paper "Towards Optimally Decentralized Multi-Robot Collision Avoidance via Deep…

LLM-RLHF-Tuning

LLM Tuning with PEFT (SFT+RM+PPO+DPO with LoRA)

Reinforcement learning library(framework) designed for PyTorch, implements DQN, DDPG, A2C, PPO, SAC, MADDPG,…

RAD: Reinforcement Learning with Augmented Data

Reinforcement_Learning

Reinforcement learning tutorials

lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.

EvoRL is a fully GPU-accelerated framework for Evolutionary Reinforcement Learning, implemented with JAX. It…

RLgraph: Modular computation graphs for deep reinforcement learning

Reinforcement learning algorithms implemented for Tensorflow 2.0+ [DQN, DDPG, AE-DDPG, SAC, PPO, Primal-Dual…

Deep Reinforcement Learning in C#

🔗 Verwandte Familien

dqn 43 a2c 18 ddpg 24 sac 21 policy-gradient 23 proximal-policy-optimization 12 a3c 19 actor-critic 20

Gemessen anhand der von beiden Projekten geteilten GitHub-Themen, gewichtet nach der Seltenheit jedes Themas.