- 21

This example will show how we can teach an AI to play a simple game using the Q-learning reinforcement learning algorithm. This is implemented in pure Ruby without any external dependencies.

https://www.practicalai.io/teaching-ai-play-simple-game-using-q-learning/https://github.com/daugaard/q-learning-simple-game

Tags | rubyml ai q-learning ml reinforcement-learning |

Implementation | Ruby |

License | Public |

Platform | Windows Linux |

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-networksReinforcement 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.

reinforcement-learning deep-reinforcement-learning sarsa q-learning policy-gradients deep-q-network deep-learning-algorithms asynchronous-advantage-actor-critic deep-deterministic-policy-gradient deep-recurrent-q-network double-dqn dueling-dqn hindsight-experience-replay drqn trpo ppoThis project follows the description of the Deep Q Learning algorithm described in Playing Atari with Deep Reinforcement Learning [2] and shows that this learning algorithm can be further generalized to the notorious Flappy Bird. It is a convolutional neural network, trained with a variant of Q-learning, whose input is raw pixels and whose output is a value function estimating future rewards.

deep-learning deep-reinforcement-learning gameIn these tutorials for reinforcement learning, it covers from the basic RL algorithms to advanced algorithms developed recent years. If you speak Chinese, visit 莫烦 Python or my Youtube channel for more.

reinforcement-learning tutorial q-learning sarsa sarsa-lambda deep-q-network a3c ddpg policy-gradient dqn double-dqn prioritized-replay dueling-dqn deep-deterministic-policy-gradient asynchronous-advantage-actor-critic actor-critic tensorflow-tutorials proximal-policy-optimization ppo machine-learningDeep Reinforcement Learning Course is a free series of blog posts and videos 🆕 about Deep Reinforcement Learning, where we'll learn the main algorithms, and how to implement them with Tensorflow. 📜The articles explain the concept from the big picture to the mathematical details behind it.

deep-reinforcement-learning qlearning deep-learning tensorflow-tutorials tensorflow ppo a2c actor-critic deep-q-network deep-q-learningI made minor tweaks to this repository such as load and save functions for convenience. I also made the memory a deque instead of just a list. This is in order to limit the maximum number of elements in the memory.

deep-reinforcement-learning deep-q-network dqn reinforcement-learning deep-learning ddqnThis repository aims to provide an introduction series to reinforcement learning (RL) by delivering a walkthough on how to code different RL techniques. A quick background review of RL is available here.

reinforcement-learning openai-gym tutorial deeplearning neural-networks deep-learning artificial-intelligence q-learning aiUpdate April 2017: It’s been almost a year since I posted this list of resources, and over the year there’s been an explosion of articles, videos, books, tutorials etc on the subject — even an explosion of ‘lists of resources’ such as this one. It’s impossible for me to keep this up to date. However, the one resource I would like to add is https://ml4a.github.io/ (https://github.com/ml4a) led by Gene Kogan. It’s specifically aimed at artists and the creative coding community. This is a very incomplete and subjective selection of resources to learn about the algorithms and maths of Artificial Intelligence (AI) / Machine Learning (ML) / Statistical Inference (SI) / Deep Learning (DL) / Reinforcement Learning (RL). It is aimed at beginners (those without Computer Science background and not knowing anything about these subjects) and hopes to take them to quite advanced levels (able to read and understand DL papers). It is not an exhaustive list and only contains some of the learning materials that I have personally completed so that I can include brief personal comments on them. It is also by no means the best path to follow (nowadays most MOOCs have full paths all the way from basic statistics and linear algebra to ML/DL). But this is the path I took and in a sense it's a partial documentation of my personal journey into DL (actually I bounced around all of these back and forth like crazy). As someone who has no formal background in Computer Science (but has been programming for many years), the language, notation and concepts of ML/SI/DL and even CS was completely alien to me, and the learning curve was not only steep, but vertical, treacherous and slippery like ice.

Qlearning4k is a reinforcement learning add-on for the python deep learning library Keras. Its simple, and is ideal for rapid prototyping. Reinforcement learning is all about training an agent to behave in an environment (such as a video game) so as to optimize a quantity (maximizing the game score), by performing certain actions in the environment (pressing buttons on the controller) and observing what happens. For every action it does, the agent gets a positive, negetive or a zero reward from the environment. These rewards help the agent understand what effect its action had on the environment, and the agent learns to do actions that are likely to result in a higher cumulative reward.

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.

reinforcement-learning deep-reinforcement-learning markov-chain temporal-differencing-learning sarsa q-learning actor-critic multi-armed-bandit inverted-pendulum mountain-car drone-landing dissecting-reinforcement-learning genetic-algorithmThis is a Tensorflow + Keras implementation of asyncronous 1-step Q learning as described in "Asynchronous Methods for Deep Reinforcement Learning". Since we're using multiple actor-learner threads to stabilize learning in place of experience replay (which is super memory intensive), this runs comfortably on a macbook w/ 4g of ram.

TensorWatch is a debugging and visualization tool designed for deep learning and reinforcement learning. It fully leverages Jupyter Notebook to show real time visualizations and offers unique capabilities to query the live training process without having to sprinkle logging statements all over. You can also use TensorWatch to build your own UIs and dashboards. In addition, TensorWatch leverages several excellent libraries for visualizing model graph, review model statistics, explain prediction and so on. TensorWatch is under heavy development with a goal of providing a research platform for debugging machine learning in one easy to use, extensible and hackable package.

ai deep-learning deeplearning machine-learning machinelearning machinelearning-python reinforcement-learning debugging debugging-tool debugger-visualizer debug monitoring explainable-ai explainable-ml saliency salient-object-detection model-visualizationTensorForce 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-networkCoach 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-ai(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.

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 examplesMachine Learning is a field of Computational Science - often nested under AI research - with many practical applications due to the ability of resulting algorithms to systematically implement a specific solution without explicit programmer's instructions. Obviously many algorithms need a definition of features to look at or a biggish training set of data to derive the solution from. This curated list comprises awesome libraries, data sources, tutorials and presentations about Machine Learning utilizing the Ruby programming language.

awesome awesome-list list ml machine-learning ruby-gem rubyml rubynlpDeep Reinforcement Learning for the JVM

reinforcement-learning deeplearning4j doom cartpole a3c dqn gym-java-client artificial-intelligenceThis 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-algorithmsWork In Progress: Crossed out items have been partially implemented. Prioritised experience replay [1] persistent advantage learning [2] bootstrapped [3] dueling [4] double [5] deep recurrent [6] Q-network [7] for the Arcade Learning Environment [8] (and custom environments). Or PERPALB(triple-D)RQN for short...

deep-reinforcement-learning deep-learning
We have large collection of open source products. Follow the tags from
Tag Cloud >>

Open source products are scattered around the web. Please provide information
about the open source projects you own / you use.
**Add Projects.**