Open-source alternatives to Mctx

This project is a Python-based educational framework designed to simulate reinforcement learning algorithms and environments. It serves as a platform for reproducing classic textbook examples, allowing users to study agent behavior, policy improvement, and the fundamental mechanics of decision-making in controlled settings. The library provides implementations for core reinforcement learning concepts, including temporal difference learning, Monte Carlo episode sampling, and tabular value function approximation. It enables the analysis of specific algorithmic behaviors, such as identifying and

This project is a reinforcement learning framework and game AI engine designed for training adversarial agents in two-player turn-based games. It implements a training loop that utilizes self-play and Monte Carlo Tree Search to produce neural networks capable of predicting board strength and move probabilities. The system decouples the reinforcement learning engine from specific game rules through an abstract game logic interface, allowing for the definition of custom game rules, win conditions, and board representations. It supports integration with various deep learning frameworks to serve

ConvNetJS is a JavaScript deep learning library and neural network training engine designed for client-side machine learning. It functions as a framework for building, training, and running convolutional neural networks directly within a web browser without the need for a backend server. The library specializes in image recognition and pattern analysis using convolutional and pooling layers. It enables the creation of models for classification and regression tasks, as well as the development of reinforcement learning agents that optimize behavior through trial and error in simulated environme

This project is a collection of TensorFlow 2.x machine learning tutorials and practical code examples. It serves as a deep learning implementation guide for constructing diverse neural network architectures, including convolutional, recurrent, and generative networks. The repository provides templates and examples for several specialized domains, including computer vision for image classification and object detection, natural language processing for text generation and language understanding, and generative AI for synthesizing data using adversarial networks and autoencoders. It also includes

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

tflearn is a deep learning framework and high-level API wrapper for TensorFlow. It provides a toolkit for designing neural network architectures and a system for executing training loops and optimizing model weights across CPUs and GPUs. The project simplifies the process of building and training models through a modular interface and a high-level API for prototyping. It includes specialized utilities for deep learning visualization, allowing for the generation of graphical diagrams to analyze network structures, weights, gradients, and activations. The framework covers a broad range of capa

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 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

DeepMimic is a deep reinforcement learning framework and physics-based motion imitation tool designed to teach simulated characters and robots to reproduce human movements. It provides a pipeline for integrating motion capture data into physics simulations to train agents that can mimic complex physical skills. The system utilizes the PyBullet simulation environment to execute motion policies and visualize character interactions in real time. It includes a motion capture integration pipeline that imports and processes animation sequences to serve as reference targets for imitation learning ag

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

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 serves as a comprehensive educational resource for learning parallel programming and high-performance computing using graphics processing units. It provides technical guidance on the fundamental paradigms required to offload computationally intensive tasks from a host system to specialized hardware accelerators. The materials cover the core methodologies for managing data-parallel operations, including the orchestration of memory between host and device spaces and the organization of threads into structured grids and blocks. It details the execution models necessary to distribute

torchdiffeq is a PyTorch ODE solver library designed for solving initial value problems and building neural ODE frameworks. It provides a differentiable ODE integrator that allows deep learning models to simulate continuous depth by integrating dynamics functions over time. The library features an adjoint method gradient calculator for memory-efficient backpropagation. By solving an augmented adjoint system backwards in time, it computes parameter gradients without storing every intermediate solver state. The project covers numerical integration with adaptive and fixed-step solvers, incorpor

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

This project is a game AI training framework designed to develop and monitor reinforcement learning agents within a legacy game environment. It functions as a training and monitoring system that optimizes autonomous agents to complete game objectives through exploration and reward-based learning. The framework includes tools for game memory mapping and real-time trajectory visualization. These capabilities translate raw game memory addresses into visual coordinates, allowing agent movements and session data to be streamed to a map for the analysis of navigation patterns and area exploration.

Tianshou is a reinforcement learning framework designed for developing and testing agents. It provides a system for implementing custom agents by defining policies and parameter update rules to optimize agent behavior. The framework decouples neural network architectures from update logic through policy-based abstractions and separates data pre-processing from gradient updates. It utilizes a collector-driven pipeline to stream experience from environments into structured memory buffers for sampled learning. The system supports vectorized environment execution to run multiple parallel instanc

This project is an educational resource designed to teach the mathematical foundations and core algorithms of reinforcement learning. It provides a structured academic curriculum that combines textbooks, lecture materials, and practical code examples to guide learners through the principles of Markov decision processes and reinforcement learning theory. The repository distinguishes itself by integrating a grid-based simulation framework that allows users to test algorithms within custom environments. This environment supports the analysis of agent performance by rendering state values, polici

This project provides a comprehensive technical guide and framework for engineering large-scale machine learning systems. It covers the full lifecycle of model development, focusing on the infrastructure and computational principles required to build, train, and serve generative AI models across distributed GPU clusters. The repository distinguishes itself by offering deep-dive tutorials and implementation strategies for complex system challenges. It emphasizes high-performance architectural primitives, such as collective communication orchestration, distributed tensor sharding, and static gr

HRM is an automated reasoning engine and language framework designed to execute complex, multi-scale problem solving. It functions as a reinforcement learning agent that continuously updates internal knowledge representations to improve task performance based on incoming data streams. The system distinguishes itself through a hierarchical architecture that coordinates abstract, long-term planning with granular, low-level logic. By integrating evolutionary algorithms and reinforcement learning, the framework refines model parameters and weights over successive generations, ensuring that intern

This project is an educational platform and research toolkit designed to teach deep learning through a combination of mathematical theory, visual diagrams, and executable code. It provides a comprehensive environment for building, training, and evaluating neural networks, grounding complex concepts in interactive computational notebooks that allow for hands-on experimentation. The framework distinguishes itself by interleaving theoretical foundations—including linear algebra, calculus, and probability—with practical implementations across multiple industry-standard libraries. It supports flex

Surge is a Swift library for high-performance numerical analysis, linear algebra, digital signal processing, and accelerated image manipulation. It utilizes the Accelerate framework to provide hardware-accelerated tools for matrix mathematics and signal processing. The library provides specialized capabilities for digital signal processing, including convolution, signal similarity analysis through cross-correlation, and domain transformations using fast Fourier transforms. It also includes a suite of tools for the rapid transformation and analysis of pixel buffers and image data. Beyond sign

This project is a game automation tool and deep reinforcement learning fishing bot designed to automate the fishing mechanics in Genshin Impact. It functions as an AI gameplay controller that simulates player inputs to collect resources without manual effort. The system utilizes a computer vision object detector to identify fish locations and rod positions on the screen. It employs deep reinforcement learning to dynamically adjust clicking force and timing based on real-time visual feedback to optimize the capture process. The automation workflow incorporates frame-based state sampling and a

This project provides a collection of practical machine learning code examples, including implementations for supervised, unsupervised, and reinforcement learning algorithms. It features deep learning model implementations for convolutional, recurrent, and generative architectures, alongside specific examples of reinforcement learning agents that maximize rewards in simulated environments. The repository includes dedicated data preprocessing pipelines for sanitization, feature scaling, and dimensionality reduction. It also provides implementations for a wide range of specific models, such as

DouZero is a deep reinforcement learning framework and training system designed to teach digital agents to master complex card games. It provides the infrastructure to implement high-throughput reinforcement learning pipelines and evaluate the competitive success of game agents. The system utilizes a distributed actor-learner architecture that separates game simulation actors from GPU training devices to accelerate model convergence. It combines Monte Carlo Tree Search with policy-based value estimation to determine optimal moves through recursive evaluation and random sampling. The toolkit

Hands-on-RL is an interactive educational resource and collection of Jupyter notebooks designed for learning reinforcement learning. It combines technical theory with practical, runnable code to demonstrate the implementation and training of mainstream reinforcement learning agents. The project focuses on bridging the gap between theory and practice through a tutorial structure that organizes explanations and executable code blocks sequentially. It enables the prototyping of reinforcement learning models to observe their behavior and performance in real-time. The implementation utilizes a mo

Pwnagotchi is an AI-powered wireless auditor and handshake capture tool that uses deep reinforcement learning to autonomously collect wireless security handshakes and crackable key material. It serves as an automation framework for network instrumentation, adapting its operational parameters based on the local wireless environment to maximize data collection. The system distinguishes itself through distributed coordination, allowing multiple hardware units to share presence and divide wireless channels to optimize collective capture and perform distributed wireless mapping. It incorporates ge

HVM2 is a high-performance execution environment for pure functional programs, implemented as a systems-level runtime in Rust. It functions as a massively parallel functional runtime that uses interaction combinators to achieve automatic parallelism across multi-core CPUs and GPUs. The project distinguishes itself by using a graph-rewriting computational model to execute programs via local reduction rules, which eliminates the need for manual locks or atomic operations. It employs beta-optimal reduction and lazy evaluation to optimize higher-order functions and eliminate redundant computation