Awesome GitHub RepositoriesPolicy Gradient Implementations

This repository provides a comprehensive library of reinforcement learning algorithms designed for training autonomous agents. It serves as a research-oriented collection of implementations that cover fundamental decision-making strategies, including dynamic programming, temporal difference learning, and policy gradient methods. The project distinguishes itself by offering specialized frameworks for deep reinforcement learning and structured decision modeling. It includes implementations for deep Q-learning that utilize neural networks, experience replay, and prioritized sampling to approxima

Stable-baselines3 is a reinforcement learning library built on the PyTorch deep learning framework. It provides a collection of reliable, standardized implementations of reinforcement learning algorithms designed for training, testing, and benchmarking agent policies in diverse simulated environments. The library functions as an agent training toolkit that emphasizes modularity and reproducibility. It features a unified environment interface and supports vectorized execution to accelerate data collection across multiple simulation instances. Users can customize neural network architectures, f

This project is an educational repository of reinforcement learning agents and tutorials implemented using TensorFlow. It provides a practical codebase for both model-free and model-based learning agents, designed to demonstrate how AI agents learn through trial and error. The collection features detailed implementations of various algorithmic approaches, including Deep Q-Networks and Policy Gradient methods. It specifically covers Actor-Critic architectures for continuous and discrete action spaces, alongside Proximal Policy Optimization and Deep Deterministic Policy Gradients. The framewor

CleanRL is a reinforcement learning library and PyTorch framework providing a suite of reproducible implementations for online reinforcement learning algorithms. It serves as a deep reinforcement learning benchmark suite and experiment orchestrator designed for research and agent development across both discrete and continuous action spaces. The project is distinguished by its single-file algorithm implementation approach, which encapsulates each algorithm in a standalone script to eliminate complex class hierarchies. This structure is paired with a system for scheduling and executing large-s

This project is a comprehensive collection of practical code examples and implementation libraries for machine learning. It provides a wide array of reference materials for building supervised, unsupervised, and reinforcement learning algorithms. The repository serves as a multi-domain resource, featuring specific implementation suites for financial AI, Bayesian statistical modeling, and deep learning architectures. It includes a framework for training intelligent agents using policy gradients and actor-critic models, as well as practical guides for fine-tuning transformers and utilizing larg

This project is a machine learning implementation library featuring a collection of code examples that implement supervised, unsupervised, and reinforcement learning algorithms from scratch. It provides a comprehensive set of toolkits for core machine learning components, including a natural language processing toolkit, a reinforcement learning framework, and suites for data dimensionality reduction and pattern mining. The library includes specialized implementations for reinforcement learning, such as Q-Learning, Deep Q-Networks, and Actor-Critic agents. The natural language processing capab

TensorLayer is a backend-agnostic tensor library and deep learning framework designed for building neural network architectures. It provides a neural network abstraction layer that allows model logic to run across different deep learning engines using high-level layers and model components. The project serves as a deep reinforcement learning toolkit for implementing policy-based, value-based, and actor-critic agents. It includes specialized tools for managing experience replay and gradient-based policy optimization to handle both discrete and continuous action spaces. To support reinforcemen

This project is a deep reinforcement learning curriculum providing educational materials and implementation exercises for mastering neural network-based agents. It serves as a framework for building reference versions of value-based and policy-based methods to solve sequential decision problems. The project provides specific implementations for continuous control simulations and multi-agent reinforcement learning, where agents are trained to cooperate or compete in shared environments. It includes a policy gradient framework for optimizing agent behavior through methods such as REINFORCE. Ca

DenseNet एक कंप्यूटर विज़न मॉडल और कन्वेन्शनल न्यूरल नेटवर्क इम्प्लीमेंटेशन है जिसे इमेज रिकग्निशन और क्लासिफिकेशन कार्यों के लिए डिज़ाइन किया गया है। यह एक डेंसली कनेक्टेड नेटवर्क आर्किटेक्चर का उपयोग करता है जहाँ फीचर प्रोपेगेशन को बेहतर बनाने के लिए प्रत्येक लेयर को हर दूसरी लेयर से जोड़ा जाता है। यह इम्प्लीमेंटेशन डेंस-कनेक्टिविटी पैटर्न और लेयर-एग्रीगेशन कॉनकैटिनेशन के माध्यम से सटीकता बनाए रखते हुए पैरामीटर्स की संख्या को कम करता है। यह इन्फरेंस टाइम और मेमोरी उपयोग को संतुलित करने के लिए कॉन्फ़िगर करने योग्य नेटवर्क डेप्थ और ग्रोथ रेट के साथ, स्टैंडर्ड और बॉटलनेक-कंप्रेस्ड आर्किटेक्चर दोनों का उपयोग करके मॉडल निर्माण का समर्थन करता है। यह प्रोजेक्ट कन्वेन्शनल न्यूरल नेटवर्क ऑप्टिमाइज़ेशन और विज़ुअल कार्यों के लिए डीप लर्निंग मॉडल ट्रेनिंग को कवर करता है। इसमें ट्रेनिंग के दौरान मेमोरी फुटप्रिंट को कम करने के लिए GPU मेमोरी ऑप्टिमाइज़ेशन तकनीकें, जैसे कि शेयर्ड ग्रेडिएंट इनपुट, शामिल हैं।

This is a structured deep learning curriculum for programmers, delivered as a collection of Jupyter notebooks. It teaches the fundamentals of training neural networks for computer vision, natural language processing, tabular data analysis, and collaborative filtering using PyTorch and the fastai library. The course is designed to be hands-on, guiding learners from building a training loop from scratch to fine-tuning pretrained models for a variety of practical tasks. The curriculum distinguishes itself by covering the full lifecycle of a deep learning project, from data preparation and augmen

mctx is a framework for executing high-performance tree search and state simulations to generate policy targets for neural networks. It functions as a compiled search engine and neural dynamics simulator that predicts state transitions and rewards using learned representations. The project implements a vectorised tree search capable of running parallel search operations across input batches. It utilizes a policy target generator to convert search results into action weights used for training and refining neural network policies. The system covers reinforcement learning workflows by integrati