#python #artificial_intelligence #atari #deep_learning #diffusion_models #machine_learning #reinforcement_learning #research #world_models

DIAMOND is a new way to train AI agents using a technique called diffusion in world models. It allows the agent to learn and play games like Atari and even simulate environments like Counter-Strike: Global Offensive. The benefit to you is that you can easily try out these pre-trained models on your own computer by following simple installation steps. You can watch the AI play, take control yourself, and even adjust how the AI imagines the game world, making it a fun and interactive way to explore advanced AI technology.

https://github.com/eloialonso/diamond

#jupyter_notebook #aws #data_science #deep_learning #examples #inference #jupyter_notebook #machine_learning #mlops #reinforcement_learning #sagemaker #training

SageMaker-Core is a new Python SDK for Amazon SageMaker that makes it easier to work with machine learning resources. It provides an object-oriented interface, which means you can manage resources like training jobs, models, and endpoints more intuitively. The SDK simplifies code by allowing resource chaining, eliminating the need to manually specify parameters. It also includes features like auto code completion, comprehensive documentation, and type hints, making it faster and less error-prone to write code. This helps developers customize their ML workloads more efficiently and streamline their development process.

https://github.com/aws/amazon-sagemaker-examples

#other #algorithms #bioinformatics #computational_biology #computational_physics #computer_architecture #computer_science #computer_vision #database_systems #databases #deep_learning #embedded_systems #machine_learning #quantum_computing #reinforcement_learning #robotics #security #systems #web_development

This collection of computer science courses offers a wide range of topics, from introductory programming to advanced specialized fields like machine learning, blockchain, and quantum computing. Here’s a simple summary of the benefits You can learn various aspects of computer science, including programming, algorithms, data structures, computer systems, software engineering, artificial intelligence, machine learning, and more.
- **Diverse Resources** There are courses on specific areas such as blockchain development, quantum computing, computational finance, and robotics, which can help you specialize in your area of interest.
- **Practical Skills** Most of these resources are available online for free, making quality education accessible to everyone.

Overall, this collection is a valuable resource for anyone looking to learn or deepen their knowledge in computer science.

https://github.com/Developer-Y/cs-video-courses

#python #3d_computer_vision #computer_vision #embodied_ai #reinforcement_learning #robot_learning #robot_manipulation #robotics #robotics_simulation #simulation_environment

ManiSkill 3 is a powerful tool for simulating and training robots, especially for tasks that involve manipulating objects. It uses GPU power to collect and simulate data very quickly, up to 30,000 frames per second, which is much faster than other simulators. This makes it great for testing and training robots in various scenarios without needing a lot of time or hardware. It supports different types of robots and tasks, and it's easy to set up and use, even on Google Colab without your own hardware. However, it's still in beta, so some features are not yet available and there might be bugs. Overall, ManiSkill 3 helps users train and test robots much more efficiently.

https://github.com/haosulab/ManiSkill

#python #baselines #gsde #gym #machine_learning #openai #python #pytorch #reinforcement_learning #reinforcement_learning_algorithms #robotics #sb3 #sde #stable_baselines #toolbox

Stable Baselines3 (SB3) is a tool that makes it easy to use reinforcement learning algorithms with PyTorch. It provides reliable and tested implementations of these algorithms, which helps researchers and developers build projects quickly. SB3 offers many features like custom environments, policies, and integration with other tools like Tensorboard and Hugging Face. It also has detailed documentation and examples to help beginners get started. This tool assumes you have some knowledge of reinforcement learning but provides resources to learn more. Using SB3 can save time and effort by providing a stable base for your projects, allowing you to focus on new ideas and improvements.

https://github.com/DLR-RM/stable-baselines3

#python #agent #agentic_ai #grpo #kimi_ai #llms #lora #qwen #qwen3 #reinforcement_learning #rl

ART is a tool that helps you train smart agents for real-world tasks using reinforcement learning, especially with the GRPO method. The standout feature is RULER, which lets you skip the hard work of designing reward functions by using a large language model to automatically score how well your agent is doing—just describe your task, and RULER takes care of the rest. This makes building and improving agents much faster and easier, works for any task, and often performs as well as or better than hand-crafted rewards. You can install ART with a simple command and start training agents right away, even on your own computer or with cloud resources.

https://github.com/OpenPipe/ART

#jupyter_notebook #chatgpt #finance #fingpt #fintech #large_language_models #machine_learning #nlp #prompt_engineering #pytorch #reinforcement_learning #robo_advisor #sentiment_analysis #technical_analysis

FinGPT is an open-source AI tool designed specifically for finance, helping you analyze financial news, predict stock prices, and get personalized investment advice quickly and affordably. Unlike costly models like BloombergGPT, FinGPT can be updated frequently with new data at a low cost, making it more accessible and timely. It uses advanced techniques like reinforcement learning from human feedback to tailor advice to your preferences, such as risk tolerance. You can use FinGPT for tasks like sentiment analysis, robo-advising, fraud detection, and portfolio optimization, helping you make smarter financial decisions with up-to-date insights.

https://github.com/AI4Finance-Foundation/FinGPT

#mdx #bilateral_teleoperation #force_feedback #genesis #gravity_compensation #humanoid_robot #imitation_learning #machine_learning #moveit2 #mujoco #open_source #openarm #python #reinforcement_learning #robot #robot_arm #robotics #ros2 #teleoperation

OpenArm is a special robot arm that helps with physical AI research. It has 7 degrees of freedom, which means it can move like a human arm. This makes it good for tasks that involve touching or moving things safely around people. The robot is open-source, meaning anyone can build, modify, and use it. This is helpful because it makes advanced robotics available to more people, like researchers and students, without costing too much. A complete system with two arms costs about $6,500, which is much cheaper than similar robots.

https://github.com/enactic/openarm

#python #agent #agentic_ai #llm #mlops #reinforcement_learning

Agent Lightning is a tool that helps improve AI agents using reinforcement learning. It allows you to train your agents without making big changes to their code, which is very convenient. You can use it with many different frameworks like LangChain or OpenAI Agent SDK. It also supports various training methods, including reinforcement learning and automatic prompt optimization. This means you can make your agents better at their tasks without a lot of extra work.

https://github.com/microsoft/agent-lightning

#python #gym #gym_environment #reinforcement_learning #reinforcement_learning_agent #reinforcement_learning_environments #rl_environment #rl_training

NeMo Gym helps you build and run reinforcement‑learning training environments for large language models, letting you develop, test, and collect verified rollouts separately from the training loop and integrate with your preferred RL framework and model endpoints (OpenAI, vLLM, etc.). It includes ready resource servers, datasets, and patterns for multi‑step, multi‑turn, and tool‑using scenarios, runs on a typical dev machine (no GPU required), and is early-stage with evolving APIs and docs. Benefit: you can generate high‑quality, verifiable training data faster and plug it into existing training pipelines to improve model behavior.

https://github.com/NVIDIA-NeMo/Gym