Search Results for "e-learning"
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Showing 89 open source projects for "e-learning"
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Best-of Machine Learning with Python
A ranked list of awesome machine learning Python libraries
...If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! General-purpose machine learning and deep learning frameworks. -
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AgentUniverse
agentUniverse is a LLM multi-agent framework
AgentUniverse is a multi-agent AI framework that enables coordination between multiple intelligent agents for complex task execution and automation. -
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Stable Baselines3
PyTorch version of Stable Baselines
Stable Baselines3 (SB3) is a set of reliable implementations of reinforcement learning algorithms in PyTorch. It is the next major version of Stable Baselines. You can read a detailed presentation of Stable Baselines3 in the v1.0 blog post or our JMLR paper. These algorithms will make it easier for the research community and industry to replicate, refine, and identify new ideas, and will create good baselines to build projects on top of. -
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Gymnasium
An API standard for single-agent reinforcement learning environments
Gymnasium is a fork of OpenAI Gym, maintained by the Farama Foundation, that provides a standardized API for reinforcement learning environments. It improves upon Gym with better support, maintenance, and additional features while maintaining backward compatibility. -
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TorchRL
A modular, primitive-first, python-first PyTorch library
TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. TorchRL provides PyTorch and python-first, low and high-level abstractions for RL that are intended to be efficient, modular, documented, and properly tested. The code is aimed at supporting research in RL. Most of it is written in Python in a highly modular way, such that researchers can easily swap components, transform them, or write new ones with little effort. -
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Jittor
Jittor is a high-performance deep learning framework
Jittor is a high-performance deep learning framework based on JIT compiling and meta-operators. The whole framework and meta-operators are compiled just in time. A powerful op compiler and tuner are integrated into Jittor. It allowed us to generate high-performance code specialized for your model. Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. ... -
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Brax
Massively parallel rigidbody physics simulation
Brax is a fast and fully differentiable physics engine for large-scale rigid body simulations, built on JAX. It is designed for research in reinforcement learning and robotics, enabling efficient simulations and gradient-based optimization. -
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verl
Volcano Engine Reinforcement Learning for LLMs
...Data pipelines treat human feedback, simulated environments, and synthetic preferences as interchangeable sources, which helps with rapid experimentation. VERL is meant for both research and production hardening: logging, checkpointing, and evaluation suites are built in so you can track learning dynamics and regressions over time. -
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BindsNET
Simulation of spiking neural networks (SNNs) using PyTorch
A Python package used for simulating spiking neural networks (SNNs) on CPUs or GPUs using PyTorch Tensor functionality. BindsNET is a spiking neural network simulation library geared towards the development of biologically inspired algorithms for machine learning. This package is used as part of ongoing research on applying SNNs to machine learning (ML) and reinforcement learning (RL) problems in the Biologically Inspired Neural & Dynamical Systems (BINDS) lab. -
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DeepSeek R1
Open-source, high-performance AI model with advanced reasoning
...The model employs a Mixture of Experts (MoE) architecture, comprising 671 billion total parameters with 37 billion active parameters per token, and supports a context length of up to 128,000 tokens. DeepSeek-R1's training regimen uniquely integrates large-scale reinforcement learning (RL) without relying on supervised fine-tuning, enabling the model to develop advanced reasoning capabilities. This approach has resulted in performance comparable to leading models like OpenAI's o1, while maintaining cost-efficiency. To further support the research community, DeepSeek has released distilled versions of the model based on architectures such as LLaMA and Qwen. -
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LightZero
[NeurIPS 2023 Spotlight] LightZero
LightZero is an efficient, scalable, and open-source framework implementing MuZero, a powerful model-based reinforcement learning algorithm that learns to predict rewards and transitions without explicit environment models. Developed by OpenDILab, LightZero focuses on providing a highly optimized and user-friendly platform for both academic research and industrial applications of MuZero and similar algorithms. -
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Habitat-Lab
A modular high-level library to train embodied AI agents
...Configuring and instantiating a diverse set of embodied agents, including commercial robots and humanoids, specifying their sensors and capabilities. Providing algorithms for single and multi-agent training (via imitation or reinforcement learning, or no learning at all as in SensePlanAct pipelines), as well as tools to benchmark their performance on the defined tasks using standard metrics. -
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Tensorforce
A TensorFlow library for applied reinforcement learning
Tensorforce is an open-source deep reinforcement learning framework built on TensorFlow, emphasizing modularized design and straightforward usability for applied research and practice. -
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dm_control
DeepMind's software stack for physics-based simulation
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo. DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo physics. The MuJoCo Python bindings support three different OpenGL rendering backends: EGL (headless, hardware-accelerated), GLFW (windowed, hardware-accelerated), and OSMesa (purely software-based). -
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Godot RL Agents
An Open Source package that allows video game creators
godot_rl_agents is a reinforcement learning integration for the Godot game engine. It allows AI agents to learn how to interact with and play Godot-based games using RL algorithms. The toolkit bridges Godot with Python-based RL libraries like Stable-Baselines3, making it possible to create complex and visually rich RL environments natively in Godot. -
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H2O LLM Studio
Framework and no-code GUI for fine-tuning LLMs
Welcome to H2O LLM Studio, a framework and no-code GUI designed for fine-tuning state-of-the-art large language models (LLMs). You can also use H2O LLM Studio with the command line interface (CLI) and specify the configuration file that contains all the experiment parameters. To finetune using H2O LLM Studio with CLI, activate the pipenv environment by running make shell. With H2O LLM Studio, training your large language model is easy and intuitive. First, upload your dataset and then start... -
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OpenRLHF
An Easy-to-use, Scalable and High-performance RLHF Framework
OpenRLHF is an easy-to-use, scalable, and high-performance framework for Reinforcement Learning with Human Feedback (RLHF). It supports various training techniques and model architectures. -
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Ray
A unified framework for scalable computing
...Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms. Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework. Scale reinforcement learning (RL) with RLlib, a framework-agnostic RL library that ships with 30+ cutting-edge RL algorithms including A3C, DQN, and PPO. Easily build out scalable, distributed systems in Python with simple and composable primitives in Ray Core. -
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DeepSeek-V3
Powerful AI language model (MoE) optimized for efficiency/performance
...The model introduces an auxiliary-loss-free load balancing strategy and a multi-token prediction training objective to boost performance. Trained on 14.8 trillion diverse, high-quality tokens, DeepSeek-V3 underwent supervised fine-tuning and reinforcement learning to fully realize its capabilities. Evaluations indicate that it outperforms other open-source models and rivals leading closed-source models, achieving this with a training duration of 55 days on 2,048 Nvidia H800 GPUs, costing approximately $5.58 million. -
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Cosmos-RL
Cosmos-RL is a flexible and scalable Reinforcement Learning framework
Cosmos-RL is a scalable reinforcement learning framework designed specifically for physical AI systems such as robotics, autonomous agents, and multimodal models. It provides a distributed training architecture that separates policy learning and environment rollout processes, enabling efficient and asynchronous reinforcement learning at scale. The framework supports multiple parallelism strategies, including tensor, pipeline, and data parallelism, allowing it to leverage large GPU clusters effectively. ... -
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AndroidEnv
RL research on Android devices
android_env is a reinforcement learning (RL) environment developed by Google DeepMind that enables agents to interact with Android applications directly as a learning environment. It provides a standardized API for training agents to perform tasks on Android apps, supporting tasks ranging from games to productivity apps, making it suitable for research in real-world RL settings. -
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RL Baselines3 Zoo
Training framework for Stable Baselines3 reinforcement learning agents
rl-baselines3-zoo is a collection of pre-trained models, benchmarks, and hyperparameter tuning tools built on top of Stable Baselines3, a reinforcement learning library. It provides an easy way to test, evaluate, and train RL agents across a wide variety of environments. -
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MedicalGPT
MedicalGPT: Training Your Own Medical GPT Model with ChatGPT Training
MedicalGPT training medical GPT model with ChatGPT training pipeline, implementation of Pretraining, Supervised Finetuning, Reward Modeling and Reinforcement Learning. MedicalGPT trains large medical models, including secondary pre-training, supervised fine-tuning, reward modeling, and reinforcement learning training. -
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robosuite
A Modular Simulation Framework and Benchmark for Robot Learning
...Developed by the ARISE Initiative, Robosuite offers a set of standardized benchmarks and customizable environments designed to advance research in robotic manipulation, control, and imitation learning. It emphasizes realistic simulations and ease of use for both single-task and multi-task learning. -
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TextWorld
TextWorld is a sandbox learning environment for the training
TextWorld is a learning environment designed to train reinforcement learning agents to play text-based games, where actions and observations are entirely in natural language. Developed by Microsoft Research, TextWorld focuses on language understanding, planning, and interaction in complex, narrative-driven environments. It generates games procedurally, enabling scalable testing of agents’ natural language processing and decision-making abilities.
