Reinforcement-learning Libraries

This course aims to introduce students to modern state of Machine Learning andArtificial Intelligence. It is designed to take one year (two terms at MIPT) -approximately 2 * 15 lectures and seminars.…

A trove of carefully curated resources and links (on the topics of software, platforms, language, techniques, etc.) related to data science, all in one place. Homework 3

It can emulate any of the following games: ['Asterix', 'Asteroids', 'MsPacman', 'Kaboom', 'BankHeist', 'Kangaroo', 'Skiing', 'FishingDerby', 'Krull', 'Berzerk', 'Tutankham', 'Zaxxon', 'Venture', 'Riv…

RL Algorithms implemented in Python for the task of global path planning for mobile robot. Such system is said to have feedback. The agent acts on the environment, and the environment acts on the age…

Deep Reinforcement Learning (DRL) agents applied to medical images tensorpack-medical requires: New contributors of any experience level are very welcomed You can clone the latest version of the sour…

This is a Python 3.0 project for analyzing stock prices and methods of stock trading. It uses native Python tools and Google TensorFlow machine learning. It has two main class modules PriceTradeAnaly…

Helpful jupyter noteboks that I compiled while learning Machine Learning and Deep Learning from various sources on the Internet. Linear & Multiple Regression Logistic Regression Regularization

Asynchronous Advantage Actor-Critic (A3C) The A3C algorithm was released by Google’s DeepMind group earlier this year, and it made a splash by essentially obsoleting DQN. It was faster, simpler, more…

We provide here a suite of Python examples that walk you through concepts in: Classical & Deep Reinforcement Learning Basic & Advanced Machine Learning Anson Wong

Minigames come as a controled environments that might be useful to exploit game features in SC2. General purpose learning system for Startcraft 2 can be a daunting task. So there is a logical option …

A collection of notebooks covering concepts that I've learned or taught

In these tutorials, we will demonstrate and visualize algorithms like Genetic Algorithm, Evolution Strategy, NEAT etc.

My Projects, Kaggle Competitions and implementation of some popularly known machine learning algorithms. I have also included a list of research papers I have curated in the feild of ML/DL/AI. Follow…

Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production models. Flexible integration for any Python script: Build …

  Get started with W&B in four steps: That's it! Navigate to the W&B App to view a dashboard of your first W&B Experiment. Use the W&B App to compare multiple experiments in a unified…

Machine Learning is a branch of Artificial Intelligence dedicated at making machines learn from observational data without being explicitly programmed. Machine learning and AI are not the same. Machi…

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMEN…

We use two metrics to evaluate the performance of an AI: Test results (averaged over 1000 episodes): Run unit tests:

A collection of machine learning examples and tutorials. Please note that not all code from all courses will be found in this repository. Some newer code examples (e.g. most of Tensorflow 2.0) were d…