Search Results for "8-puzzle reinforcement learning python"
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Showing 17 open source projects for "8-puzzle reinforcement learning python"
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Hello Python
Comprehensive tutorial repository aimed at teaching the Python program
Hello-Python is a comprehensive tutorial repository aimed at teaching the Python programming language from scratch for beginners. It includes over 100 classes and about 44 hours of video instruction, combined with code samples, projects, and a chat community for support. The material covers the fundamentals—variables, data types, loops, functions—as well as intermediate topics like date handling, list comprehensions, file IO, regular expressions, modules, and packages. The course is designed... -
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Complete-Python-3-Bootcamp
Course Files for Complete Python 3 Bootcamp Course on Udemy
The Complete-Python-3-Bootcamp repository is an educational resource created by Pierian Data as part of their popular Python for Data Science and Machine Learning Bootcamp course. It contains a comprehensive collection of Jupyter Notebooks designed to teach Python programming from the ground up. The repository covers a wide range of Python topics, including data types, control flow, functions, object-oriented programming, error handling, modules, and advanced concepts like decorators and... -
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ML for Beginners
12 weeks, 26 lessons, 52 quizzes, classic Machine Learning for all
ML-For-Beginners is a structured, project-driven curriculum that teaches foundational machine learning concepts with approachable math and lots of code. Organized as a multi-week course, it mixes short lectures with labs in notebooks so learners practice regression, classification, clustering, and recommendation techniques on real datasets. Each lesson aims to connect the algorithm to a relatable scenario, reinforcing intuition before diving into parameters, metrics, and trade-offs. The... -
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Summarize from Feedback
Code for "Learning to summarize from human feedback"
The summarize-from-feedback repository implements the methods from the paper “Learning to Summarize from Human Feedback”. Its purpose is to train a summarization model that better aligns with human preferences by first collecting human feedback (comparisons between summaries) to train a reward model, and then fine-tuning a policy (summarizer) to maximize that learned reward. The code includes different stages: a supervised baseline (i.e. standard summarization training), the reward modeling... -
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Reinforcement Learning Methods
Simple Reinforcement learning tutorials
Reinforcement-Learning-with-TensorFlow is an educational repository that walks through key reinforcement learning algorithms implemented in TensorFlow. It provides clear code examples for foundational techniques like Q-learning, policy gradients, deep Q-networks, actor-critic methods, and value function approximation within familiar simulation environments. Each algorithm is structured with readable code, explanatory comments, and corresponding environment interaction loops so learners can... -
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DeepMind Educational Resources
DeepMind's repo of educational notebooks for learning AI and research
Educational is an open collection of interactive tutorials created by Google DeepMind to make the fundamentals of machine learning and artificial intelligence accessible to learners of all backgrounds. The repository provides hands-on, beginner-friendly resources that introduce essential AI concepts through Google Colab notebooks, combining intuitive explanations with executable code. The tutorials cover a broad range of topics—from foundational Python programming and data handling to supervised, unsupervised, and reinforcement learning, as well as graph neural networks and scientific reasoning. ... -
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Brain Tokyo Workshop
Experiments and code from Google Brain’s Tokyo research workshop
The Brain Tokyo Workshop repository hosts a collection of research materials and experimental code developed by the Google Brain team based in Tokyo. It showcases a variety of cutting-edge projects in artificial intelligence, particularly in the areas of neuroevolution, reinforcement learning, and model interpretability. Each project explores innovative approaches to learning, prediction, and creativity in neural networks, often through unconventional or biologically inspired methods. The... -
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Catalyst
Accelerated deep learning R&D
Catalyst is a PyTorch framework for accelerated Deep Learning research and development. It allows you to write compact but full-featured Deep Learning pipelines with just a few lines of code. With Catalyst you get a full set of features including a training loop with metrics, model checkpointing and more, all without the boilerplate. Catalyst is focused on reproducibility, rapid experimentation, and codebase reuse so you can break the cycle of writing another regular train loop and make... -
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Machine Learning PyTorch Scikit-Learn
Code Repository for Machine Learning with PyTorch and Scikit-Learn
Initially, this project started as the 4th edition of Python Machine Learning. However, after putting so much passion and hard work into the changes and new topics, we thought it deserved a new title. So, what’s new? There are many contents and additions, including the switch from TensorFlow to PyTorch, new chapters on graph neural networks and transformers, a new section on gradient boosting, and many more that I will detail in a separate blog post. For those who are interested in knowing... -
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DeepMind Lab
A customizable 3D platform for agent-based AI research
DeepMind Lab is a 3D learning environment based on id Software's Quake III Arena via ioquake3 and other open source software. DeepMind Lab provides a suite of challenging 3D navigation and puzzle-solving tasks for learning agents. Its primary purpose is to act as a testbed for research in artificial intelligence, especially deep reinforcement learning. If you use DeepMind Lab in your research and would like to cite the DeepMind Lab environment, we suggest you cite the DeepMind Lab paper. ... -
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Python Tutorials
Machine Learning Tutorials
...It also provides tutorials on machine learning frameworks and concepts, including TensorFlow, PyTorch, Keras, Scikit-Learn, and reinforcement learning techniques. Each section contains organized code and explanations designed to help learners understand the underlying mechanics of Python and common computational approaches. -
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Tensorflow 2017 Tutorials
Tensorflow tutorial from basic to hard
Tensorflow 2017 Tutorials is a structured set of tutorials that introduce developers to TensorFlow, starting with basic neural network constructs and progressing to sophisticated model architectures and training techniques. This repository covers essential building blocks like sessions (for older TF versions), placeholders, variables, activation functions, and optimizers, before guiding learners through building end-to-end models for regression, classification, and data pipelines. Beyond the... -
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Tensorflow and deep learning
A crash course in six episodes for software developers
Tensorflow and deep learning repository is an educational deep learning crash course designed to help software developers quickly understand and apply machine learning concepts without requiring advanced academic background. It is structured as a series of guided lessons that combine theoretical explanations, practical examples, and runnable code, allowing learners to build intuition while actively experimenting with models. The repository covers core neural network concepts such as weights,... -
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Spinning Up in Deep RL
Educational resource to help anyone learn deep reinforcement learning
Welcome to Spinning Up in Deep RL! This is an educational resource produced by OpenAI that makes it easier to learn about deep reinforcement learning (deep RL). For the unfamiliar, reinforcement learning (RL) is a machine learning approach for teaching agents how to solve tasks by trial and error. Deep RL refers to the combination of RL with deep learning. At OpenAI, we believe that deep learning generally, and deep reinforcement learning specifically, will play central roles in the... -
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Neural MMO
Code for the paper "Neural MMO: A Massively Multiagent Game..."
Neural MMO is a massively multi-agent simulation environment developed by OpenAI for reinforcement learning research. It provides a persistent, procedurally generated world where thousands of agents can interact, compete, and cooperate in real time. The environment is inspired by Massively Multiplayer Online Role-Playing Games (MMORPGs), featuring resource gathering, combat mechanics, exploration, and survival challenges. Agents learn behaviors in a shared ecosystem that supports long-term... -
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Evolution Strategies Starter
Code for the paper "Evolution Strategies.."
evolution-strategies-starter is an archived OpenAI research project that provides a distributed implementation of the algorithm described in the paper “Evolution Strategies as a Scalable Alternative to Reinforcement Learning” by Tim Salimans, Jonathan Ho, Xi Chen, and Ilya Sutskever. The repository demonstrates how to scale Evolution Strategies (ES) for reinforcement learning tasks using a master-worker architecture, where the master node broadcasts parameters to multiple workers, and the... -
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PySet is an incremental implementation of the classic logic/card game Set, built using Python, Simple Direct-media Library (SDL), and PyGame (a Python wrapper of the SDL API). The code is intended to assist newcomers in gradually learning these libs.
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