pytorch-a3c-mujoco - Implement A3C for Mujoco gym envs

  •        41

Note that this repo is only compatible with Mujoco in OpenAI gym. If you want to train agent in Atari domain, please refer to pytorch-a3c. There're three tasks/modes for you: train, eval, develop.

https://github.com/andrewliao11/pytorch-a3c-mujoco

Tags
Implementation
License
Platform

   




Related Projects

pytorch-maml-rl - Reinforcement Learning with Model-Agnostic Meta-Learning in Pytorch

  •    Python

Implementation of Model-Agnostic Meta-Learning (MAML) applied on Reinforcement Learning problems in Pytorch. This repository includes environments introduced in (Duan et al., 2016, Finn et al., 2017): multi-armed bandits, tabular MDPs, continuous control with MuJoCo, and 2D navigation task. Create a virtual environment, activate it and install the requirements in requirements.txt.

GA3C - Hybrid CPU/GPU implementation of the A3C algorithm for deep reinforcement learning.

  •    Python

A hybrid CPU/GPU version of the Asynchronous Advantage Actor-Critic (A3C) algorithm, currently the state-of-the-art method in reinforcement learning for various gaming tasks. This CPU/GPU implementation, based on TensorFlow, achieves a significant speed up compared to a similar CPU implementation. Run sh _clean.sh first, and then sh _train.sh. The script _clean.sh cleans the checkpoints folder, which contains the network models saved during the training process, as well as removing results.txt, which is a log of the scores achieved during training.

Hands-On-Reinforcement-Learning-With-Python - Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow

  •    Jupyter

Reinforcement Learning with Python will help you to master basic reinforcement learning algorithms to the advanced deep reinforcement learning algorithms. The book starts with an introduction to Reinforcement Learning followed by OpenAI and Tensorflow. You will then explore various RL algorithms and concepts such as the Markov Decision Processes, Monte-Carlo methods, and dynamic programming, including value and policy iteration. This example-rich guide will introduce you to deep learning, covering various deep learning algorithms. You will then explore deep reinforcement learning in depth, which is a combination of deep learning and reinforcement learning. You will master various deep reinforcement learning algorithms such as DQN, Double DQN. Dueling DQN, DRQN, A3C, DDPG, TRPO, and PPO. You will also learn about recent advancements in reinforcement learning such as imagination augmented agents, learn from human preference, DQfD, HER and many more.


DDPG - Reimplementation of DDPG(Continuous Control with Deep Reinforcement Learning) based on OpenAI Gym + Tensorflow

  •    Python

It is still a problem to implement Batch Normalization on the critic network. However the actor network works well with Batch Normalization. Some Mujoco environments are still unsolved on OpenAI Gym.

async-rl - Replicating "Asynchronous Methods for Deep Reinforcement Learning" (http://arxiv

  •    Python

(2017/02/25) Now the A3C implementation in this repository has been ported into ChainerRL, a Chainer-based deep reinforcement learning library, with some enhancement such as support for continuous actions by Gaussian policies and n-step Q-learning, so I recommend using it instead of this repository. This is a repository where I attempt to reproduce the results of Asynchronous Methods for Deep Reinforcement Learning. Currently I have only replicated A3C FF/LSTM for Atari.

rl-teacher - Code for Deep RL from Human Preferences [Christiano et al]

  •    Python

rl-teacher is an implementation of Deep Reinforcement Learning from Human Preferences [Christiano et al., 2017]. Obtain a license for MuJoCo and install the binaries on your system. For good documentation on MuJoCo installation, and an easy way to test that MuJoCo is working on your system, we recommend following the mujoco-py installation.

Meta-RL - Implementation of Meta-RL A3C algorithm

  •    Jupyter

Tensorflow implementation of Meta-RL A3C algorithm taken from Learning to Reinforcement Learn. For more information, as well as explainations of each of the experiments, see my corresponding Medium post. A3C is built from previous implementation available here.

dm_control - The DeepMind Control Suite and Package

  •    Python

A set of Python Reinforcement Learning environments powered by the MuJoCo physics engine. See the suite subdirectory. Libraries that provide Python bindings to the MuJoCo physics engine.

deep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program

  •    Jupyter

This repository contains material related to Udacity's Deep Reinforcement Learning Nanodegree program. The tutorials lead you through implementing various algorithms in reinforcement learning. All of the code is in PyTorch (v0.4) and Python 3.

coach - Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms

  •    Python

Coach is a python reinforcement learning research framework containing implementation of many state-of-the-art algorithms. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms, and allows simple integration of new environments to solve. Basic RL components (algorithms, environments, neural network architectures, exploration policies, ...) are well decoupled, so that extending and reusing existing components is fairly painless.

mujoco-py - MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts

  •    Python

MuJoCo is a physics engine for detailed, efficient rigid body simulations with contacts. mujoco-py allows using MuJoCo from Python 3. Python 2 has been desupported since 1.50.1.0. Python 2 users can stay on the 0.5 branch. The latest release there is 0.5.7 which can be installed with pip install mujoco-py==0.5.7.

alpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4

  •    Python

A simplified, highly flexible, commented and (hopefully) easy to understand implementation of self-play based reinforcement learning based on the AlphaGo Zero paper (Silver et al). It is designed to be easy to adopt for any two-player turn-based adversarial game and any deep learning framework of your choice. A sample implementation has been provided for the game of Othello in PyTorch, Keras and TensorFlow. An accompanying tutorial can be found here. We also have implementations for GoBang and TicTacToe. To use a game of your choice, subclass the classes in Game.py and NeuralNet.py and implement their functions. Example implementations for Othello can be found in othello/OthelloGame.py and othello/{pytorch,keras,tensorflow}/NNet.py.

Practical_RL - A course in reinforcement learning in the wild

  •    Jupyter

A course on reinforcement learning in the wild. Taught on-campus at HSE and YSDA and maintained to be friendly to online students (both english and russian). The syllabus is approximate: the lectures may occur in a slightly different order and some topics may end up taking two weeks.

tensorlayer - Deep Learning and Reinforcement Learning Library for Developers and Scientists

  •    Python

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides a large collection of customizable neural layers / functions that are key to build real-world AI applications. TensorLayer is awarded the 2017 Best Open Source Software by the ACM Multimedia Society. Simplicity : TensorLayer lifts the low-level dataflow interface of TensorFlow to high-level layers / models. It is very easy to learn through the rich example codes contributed by a wide community.

torch-light - Deep-learning by using Pytorch

  •    Python

This repository includes basics and advanced examples for deep learning by using Pytorch. Basics which are basic nns like Logistic, CNN, RNN, LSTM are implemented with few lines of code, advanced examples are implemented by complex model. It is better finish Official Pytorch Tutorial before this.

dissecting-reinforcement-learning - Python code, PDFs and resources for the series of posts on Reinforcement Learning which I published on my personal blog

  •    Python

This repository contains the code and pdf of a series of blog post called "dissecting reinforcement learning" which I published on my blog mpatacchiola.io/blog. Moreover there are links to resources that can be useful for a reinforcement learning practitioner. If you have some good references which may be of interest please send me a pull request and I will integrate them in the README. The source code is contained in src with the name of the subfolders following the post number. In pdf there are the A3 documents of each post for offline reading. In images there are the raw svg file containing the images used in each post.

PyTorch-Tutorial - Build your neural network easy and fast

  •    Jupyter

In these tutorials for pyTorch, we will build our first Neural Network and try to build some advanced Neural Network architectures developed recent years. Thanks for liufuyang's notebook files which is a great contribution to this tutorial.