There are 6 repositories under proximal-policy-optimization topic.
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
PyTorch implementation of Deep Reinforcement Learning: Policy Gradient methods (TRPO, PPO, A2C) and Generative Adversarial Imitation Learning (GAIL). Fast Fisher vector product TRPO.
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)
A PyTorch library for building deep reinforcement learning agents.
PyTorch C++ Reinforcement Learning
PyTorch implementations of various Deep Reinforcement Learning (DRL) algorithms for both single agent and multi-agent.
lagom: A PyTorch infrastructure for rapid prototyping of reinforcement learning algorithms.
Trading Environment(OpenAI Gym) + PPO(TensorForce)
Deep Reinforcement Learning (PPO) in Autonomous Driving (Carla) [from scratch]
Recurrent and multi-process PyTorch implementation of deep reinforcement Actor-Critic algorithms A2C and PPO
Curiosity-driven Exploration by Self-supervised Prediction
Proximal Policy Optimization (PPO) algorithm for Contra
PyTorch implementation of some reinforcement learning algorithms: A2C, PPO, Behavioral Cloning from Observation (BCO), GAIL.
Proximal Policy Optimization(PPO) with Intrinsic Curiosity Module(ICM)
Clean baseline implementation of PPO using an episodic TransformerXL memory
Baseline implementation of recurrent PPO using truncated BPTT
Code for the paper "Reinforced Curriculum Learning for Autonomous Driving in CARLA" (ICIP 2021)
This is an pytorch implementation of Distributed Proximal Policy Optimization(DPPO).
Reinforcement Learning Agents in .NET
强化学习算法库,包含了目前主流的强化学习算法(Value based and Policy based)的代码,代码都经过调试并可以运行
Deep Reinforcement Learning by using Proximal Policy Optimization and Random Network Distillation in Tensorflow 2 and Pytorch with some explanation
Implementation of a Deep Reinforcement Learning algorithm, Proximal Policy Optimization (SOTA), on a continuous action space openai gym (Box2D/Car Racing v0)
An implementation of Phasic Policy Gradient, a proposed improvement of Proximal Policy Gradients, in Pytorch
It's the pytorch implementation of google research football.
Multi-Agent Deep Reinforcement Learning by using Asynchronous & Impala Proximal Policy Optimization in Pytorch with some explanation
Proximal Policy Optimization with Tensorflow 2.0