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ELF - An End-To-End, Lightweight and Flexible Platform for Game Research
ELF is an Extensive, Lightweight and Flexible platform for game research, in particular for real-time strategy (RTS) games. On the C++-side, ELF hosts multiple games in parallel with C++ threading. On the Python side, ELF returns one batch of game state at a time, making it very friendly for modern RL. In comparison, other platforms (e.g., OpenAI Gym) wraps one single game instance with one Python interface. This makes concurrent game execution a bit complicated, which is a requirement of many modern reinforcement learning algorithms. Besides, ELF now also provides a Python version for running concurrent game environments, by Python multiprocessing with ZeroMQ inter-process communication. See ./ex_elfpy.py for a simple example.
gaming cpp artificial-intelligence deep-learning neural-network platform reinforcement-learningawesome-artificial-intelligence - A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers
A curated list of Artificial Intelligence (AI) courses, books, video lectures and papers. Contributions most welcome.
machine-learning machine-intelligence artificial-intelligence reinforcement-learning intelligent-systems deep-learning intelligent-machineskeras-rl - Deep Reinforcement Learning for Keras.
keras-rl implements some state-of-the art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Just like Keras, it works with either Theano or TensorFlow, which means that you can train your algorithm efficiently either on CPU or GPU. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and even algorithms by simply extending some simple abstract classes. In a nutshell: keras-rl makes it really easy to run state-of-the-art deep reinforcement learning algorithms, uses Keras and thus Theano or TensorFlow and was built with OpenAI Gym in mind.
keras tensorflow theano reinforcement-learning neural-networks machine-learningTensorFlow-Book - Accompanying source code for Machine Learning with TensorFlow
This is the official code repository for Machine Learning with TensorFlow. Get started with machine learning using TensorFlow, Google's latest and greatest machine learning library.
tensorflow machine-learning regression convolutional-neural-networks logistic-regression book reinforcement-learning autoencoder linear-regression classification clusteringtensorpack - A Neural Net Training Interface on TensorFlow
Tensorpack is a training interface based on TensorFlow. It's Yet Another TF high-level API, with speed, readability and flexibility built together.
tensorflow imagenet deep-learning reinforcement-learning neural-networks machine-learningml-agents - Unity Machine Learning Agents
Unity Machine Learning Agents (ML-Agents) is an open-source Unity plugin that enables games and simulations to serve as environments for training intelligent agents. Agents can be trained using reinforcement learning, imitation learning, neuroevolution, or other machine learning methods through a simple-to-use Python API. We also provide implementations (based on TensorFlow) of state-of-the-art algorithms to enable game developers and hobbyists to easily train intelligent agents for 2D, 3D and VR/AR games. These trained agents can be used for multiple purposes, including controlling NPC behavior (in a variety of settings such as multi-agent and adversarial), automated testing of game builds and evaluating different game design decisions pre-release. ML-Agents is mutually beneficial for both game developers and AI researchers as it provides a central platform where advances in AI can be evaluated on Unity’s rich environments and then made accessible to the wider research and game developer communities. For more information, in addition to installation and usage instructions, see our documentation home. If you have used a version of ML-Agents prior to v0.3, we strongly recommend our guide on migrating to v0.3.
reinforcement-learning unity3d deep-learning unity deep-reinforcement-learning neural-networksFlappyBirdRL - Flappy Bird hack using Reinforcement Learning
More details here. If you just opened the index.html page you might have an error in the console regarding origin requests. Right click anywhere on the screen, click Inspect and then look at the Console.
machine-learning reinforcement-learning flappy-birddeep-reinforcement-learning - Repo for the Deep Reinforcement Learning Nanodegree program
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.
deep-reinforcement-learning reinforcement-learning reinforcement-learning-algorithms neural-networks pytorch pytorch-rl ddpg dqn ppo dynamic-programming cross-entropy hill-climbing ml-agents openai-gym-solutions openai-gym rl-algorithmspysc2 - StarCraft II Learning Environment
PySC2 is DeepMind's Python component of the StarCraft II Learning Environment (SC2LE). It exposes Blizzard Entertainment's StarCraft II Machine Learning API as a Python RL Environment. This is a collaboration between DeepMind and Blizzard to develop StarCraft II into a rich environment for RL research. PySC2 provides an interface for RL agents to interact with StarCraft 2, getting observations and sending actions. We have published an accompanying blogpost and paper, which outlines our motivation for using StarCraft II for DeepRL research, and some initial research results using the environment.
reinforcement-learning machine-learning starcraft-ii starcraft-ii-replays deepmind blizzard-apicoach - Reinforcement Learning Coach by Intel® AI Lab enables easy experimentation with state of the art Reinforcement Learning algorithms
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.
coach openai-gym reinforcement-learning tensorflow rl carla imitation-learning mujoco roboschool deep-learning hierarchical-reinforcement-learning starcraft starcraft2 starcraft2-aiTensorFlow-Tutorials - TensorFlow Tutorials with YouTube Videos
TensorFlow Tutorials with YouTube Videos
tensorflow deep-learning machine-learning reinforcement-learning python-notebook tutorial neural-network youtubepolyaxon - An open source platform for reproducible machine learning and deep learning on kubernetes
Welcome to Polyaxon, a platform for building, training, and monitoring large scale deep learning applications. Polyaxon deploys into any data center, cloud provider, or can be hosted and managed by Polyaxon, and it supports all the major deep learning frameworks such as Tensorflow, MXNet, Caffe, Torch, etc.
deep-learning machine-learning artificial-intelligence data-science reinforcement-learning kubernetes tensorflow pytorch keras mxnet caffe ai dl ml k8sPractical_RL - A course in reinforcement learning in the wild
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.
reinforcement-learning course-materials deep-learning deep-reinforcement-learning git-course mooc theano lasagne tensorflow pytorch pytorch-tutorials kerasELF - ELF: a platform for game research with AlphaGoZero/AlphaZero reimplementation
ELF is an Extensive, Lightweight, and Flexible platform for game research. We have used it to build our Go playing bot, ELF OpenGo, which achieved a 14-0 record versus four global top-30 players in April 2018. The final score is 20-0 (each professional Go players play 5 games). We have released our v0 models here.
reinforcement-learning alphago-zero rl rl-environment alpha-zeroMAgent - A Platform for Many-agent Reinforcement Learning
MAgent is a research platform for many-agent reinforcement learning. Unlike previous research platforms that focus on reinforcement learning research with a single agent or only few agents, MAgent aims at supporting reinforcement learning research that scales up from hundreds to millions of agents. MAgent supports Linux and OS X running Python 2.7 or python 3. We make no assumptions about the structure of your agents. You can write rule-based algorithms or use deep learning frameworks.
reinforcement-learning multi-agent deep-learningBullet Physics SDK - Real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc
Bullet Physics SDK: real-time collision detection and multi-physics simulation for VR, games, visual effects, robotics, machine learning etc.
simulation robotics kinematics virtual-reality reinforcement-learning computer-animation game-developmenttensorforce - TensorForce: A TensorFlow library for applied reinforcement learning
TensorForce is an open source reinforcement learning library focused on providing clear APIs, readability and modularisation to deploy reinforcement learning solutions both in research and practice. TensorForce is built on top of TensorFlow and compatible with Python 2.7 and >3.5 and supports multiple state inputs and multi-dimensional actions to be compatible with any type of simulation or application environment. TensorForce also aims to move all reinforcement learning logic into the TensorFlow graph, including control flow. This both reduces dependencies on the host language (Python), thus enabling portable computation graphs that can be used in other languages and contexts, and improves performance.
reinforcement-learning tensorflow deep-reinforcement-learning deep-q-networkViZDoom - Doom-based AI Research Platform for Reinforcement Learning from Raw Visual Information
ViZDoom allows developing AI bots that play Doom using only the visual information (the screen buffer). It is primarily intended for research in machine visual learning, and deep reinforcement learning, in particular. ViZDoom is based on ZDoom to provide the game mechanics.
deep-learning reinforcement-learning vizdoom doom examplesDeepRL-Agents - A set of Deep Reinforcement Learning Agents implemented in Tensorflow.
This repository contains a collection of reinforcement learning algorithms written in Tensorflow. The ipython notebook here were written to go along with a still-underway tutorial series I have been publishing on Medium. If you are new to reinforcement learning, I recommend reading the accompanying post for each algorithm.
reinforcement-learning tensorflowalpha-zero-general - A clean implementation based on AlphaZero for any game in any framework + tutorial + Othello/Gobang/TicTacToe/Connect4
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.
tensorflow pytorch keras gobang gomoku alpha-zero alphago-zero alphago reinforcement-learning self-play mcts monte-carlo-tree-search othello tf deep-learning alphazero
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