Projects sharing features with Coach

Lab is a customizable 3D platform and research testbed designed for training and testing autonomous agents using reinforcement learning. It serves as a spatial AI training simulator where agents can be evaluated through navigation and puzzle-solving tasks. The environment allows for the definition of complex layouts and task behaviors through external scripting, enabling the generation of specific challenges for AI research. It supports both automated training via standard API bindings and manual agent control to validate simulation dynamics. The system utilizes a grid-based spatial represen

Deep Reinforcement Learning for Keras.

Maze Applied Reinforcement Learning Framework

SerpentAI is a game AI development kit and computer vision framework designed for building autonomous agents that interact with video games. It serves as a game input automation tool and a machine learning model integration engine, allowing developers to create agents that perceive game states and execute actions. The framework utilizes a plugin-based agent architecture to provide modular extensions for game-specific logic and behaviors. It features a specialized system for training, bundling, and deploying machine learning classifiers to recognize visual contexts and game states in real time

This project is a reinforcement learning toolkit and simulation-based AI trainer for creating intelligent agents within Unity simulations. It provides a multi-agent simulation framework for configuring cooperative or competitive scenarios and includes an environment wrapper that bridges simulations with standard machine learning libraries using gym-style interfaces. The system features a native cross-platform inference engine that executes trained neural network models for real-time decision making without external dependencies. It enables the acceleration of the learning process by running m

OpenDILab Decision AI Engine. The Most Comprehensive Reinforcement Learning Framework B.P.

TF-Agents: A reliable, scalable and easy to use TensorFlow library for Contextual Bandits and Reinforcement Learning.

Modular Deep Reinforcement Learning framework in PyTorch. Companion library of the book "Foundations of Deep Reinforcement Learning".

Practical tutorials and labs for TensorFlow used by Nvidia, FFN, CNN, RNN, Kaggle, AE

This repository contains the implementation of DISCERN in Python. You can download the manuscript from my website or arXiv.

Build fully-functioning computer vision models with PyTorch

DAMO-ConvAI: The official repository which contains the codebase for Alibaba DAMO Conversational AI.

ROLL is a distributed reinforcement learning framework and model alignment toolkit designed for large language models. It serves as a scalable training pipeline and GPU cluster manager, providing the infrastructure to align model behavior using reinforcement learning algorithms and preference optimization techniques. The project distinguishes itself through an agentic rollout orchestrator that generates and collects multi-turn interaction trajectories between AI agents and simulated environments. It supports specialized alignment methods including Direct Preference Optimization, reinforcement

A short and easy implementation of Quantile Regression DQN | Distributional Reinforcement Learning

A replica of the AlphaZero methodology for deep reinforcement learning in Python

Reinforcement Learning in PyTorch

Reinforcement Learning in Go

Hyperparameter Experiments with TensorFlow and Keras

You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery

A set of Deep Reinforcement Learning Agents implemented in Tensorflow.

This repository serves as a structured educational resource for machine learning and deep learning, providing a library of executable scripts and notebooks. It is designed to help users master the practical application of data processing, model evaluation, and neural network construction through annotated code samples and guided tutorials. The collection focuses on translating theoretical mathematical concepts into functional code, offering proven patterns for common tasks such as classification and regression. By providing curated examples of layer construction and training loops, the reposi

A flexible and efficient training framework for large-scale alignment tasks