Best AI tools for< Reinforcement Learning Engineer >

PyTorch is an open-source machine learning library based on the Torch library. It is used for applications such as computer vision, natural language processing, and reinforcement learning. PyTorch is known for its flexibility and ease of use, making it a popular choice for researchers and developers in the field of artificial intelligence.

TWIML is a platform that provides intelligent content focusing on Machine Learning and Artificial Intelligence technologies. It offers podcasts, articles, and resources to practitioners, innovators, and leaders, giving insights into the present and future of ML & AI. The platform covers a wide range of topics such as deep reinforcement learning, fusion energy production, data-centric AI, responsible AI, and machine learning platform strategies.

Google DeepMind is a British artificial intelligence research laboratory owned by Google. The company was founded in 2010 by Demis Hassabis, Shane Legg, and Mustafa Suleyman. DeepMind's mission is to develop safe and beneficial artificial intelligence. The company's research focuses on a variety of topics, including machine learning, reinforcement learning, and computer vision. DeepMind has made significant contributions to the field of artificial intelligence, including the development of AlphaGo, the first computer program to defeat a professional human Go player.

Winder.ai is an award-winning Enterprise AI Agency that specializes in AI development, consulting, and product development. They have expertise in Reinforcement Learning, MLOps, and Data Science, offering services to help businesses automate processes, scale products, and unlock new markets. With a focus on delivering AI solutions at scale, Winder.ai collaborates with clients globally to enhance operational efficiency and drive innovation through AI technologies.

Amazon Science is a research and development organization within Amazon that focuses on developing new technologies and products in the fields of artificial intelligence, machine learning, and computer science. The organization is home to a team of world-renowned scientists and engineers who are working on a wide range of projects, including developing new algorithms for machine learning, building new computer vision systems, and creating new natural language processing tools. Amazon Science is also responsible for developing new products and services that use these technologies, such as the Amazon Echo and the Amazon Fire TV.

MIRI (Machine Intelligence Research Institute) is a non-profit research organization dedicated to ensuring that artificial intelligence has a positive impact on humanity. MIRI conducts foundational mathematical research on topics such as decision theory, game theory, and reinforcement learning, with the goal of developing new insights into how to build safe and beneficial AI systems.

Artificial Intelligence: A Modern Approach, 4th US ed. is the authoritative, most-used AI textbook, adopted by over 1500 schools. It covers the entire spectrum of AI, from the fundamentals to the latest advances. The book is written in a clear and concise style, with a wealth of examples and exercises. It is suitable for both undergraduate and graduate students, as well as professionals in the field of AI.

StartKit.AI is a boilerplate code for AI products that helps users build their AI startups 100x faster. It includes pre-built REST API routes for all common AI functionality, a pre-configured Pinecone for text embeddings and Retrieval-Augmented Generation (RAG) for chat endpoints, and five React demo apps to help users get started quickly. StartKit.AI also provides a license key and magic link authentication, user & API limit management, and full documentation for all its code. Additionally, users get access to guides to help them get set up and one year of updates.

Permar is an AI-powered website optimization tool that helps businesses increase their conversion rates. It uses reinforcement learning techniques to dynamically adapt website optimization, resulting in an average uplift in conversion rates of 10-12% compared to static A/B tests. Permar also offers a complete toolkit of features to help businesses create high-converting landing pages, including dynamic A/B testing, real-time optimization, and growth experiment ideas.

Lyzr AI is a full-stack agent framework designed to build GenAI applications faster. It offers a range of AI agents for various tasks such as chatbots, knowledge search, summarization, content generation, and data analysis. The platform provides features like memory management, human-in-loop interaction, toxicity control, reinforcement learning, and custom RAG prompts. Lyzr AI ensures data privacy by running data locally on cloud servers. Enterprises and developers can easily configure, deploy, and manage AI agents using Lyzr's platform.

VAPA is an AI-powered Amazon PPC tool that helps businesses optimize their Amazon advertising campaigns. It leverages deep reinforcement learning algorithms to maximize campaign performance and increase sales. VAPA offers fully automated ad management, strategic growth insights, and expert campaign optimization across multiple Amazon marketplaces. The tool is designed to streamline the advertising process, save time, and improve overall efficiency in managing Amazon ads.

AIAI.Tools is a comprehensive directory of AI-powered tools and applications designed to enhance various aspects of work and productivity. The platform features a wide range of AI tools spanning different categories such as SEO, productivity, research, automation, and development. Users can explore and discover innovative AI solutions that leverage cutting-edge technologies like reinforcement learning algorithms, ChatGPT AI technology, and video search capabilities. AIAI.Tools aims to simplify tasks, improve efficiency, and streamline workflows by providing access to advanced AI tools that cater to diverse needs across industries.

ToyPal is an AI-powered storytelling toy designed for kids to enhance their learning experience and promote positive habits. It allows parents to craft personalized stories featuring their child's name, guiding them towards healthy routines. The application focuses on improving children's behavior through engaging and imaginative storytelling, creating a unique bond between the child and their favorite soft toy. ToyPal offers a safe and screen-free environment, with a rich library of 500+ free stories and a supportive parent community.

SpamSpy is a community-powered AI platform designed to combat spam and malicious content online. It offers a range of pricing plans to suit different needs, from free options to enterprise solutions. The platform leverages supervised and unsupervised algorithms for accurate assessment of relevance, and personalized reinforcement models to enhance response quality. SpamSpy encourages community involvement and contributions to improve its spam detection capabilities.

Dropzone AI is an award-winning AI application designed to reinforce Security Operations Centers (SOCs) by providing autonomous AI analysts. It replicates the techniques of elite analysts to autonomously investigate alerts, covering various use cases such as phishing, endpoint, network, cloud, identity, and insider threats. The application offers pre-trained AI agents that work alongside human analysts, automating investigation tasks and providing fast, detailed, and accurate reports. With built-in integrations with major security tools, Dropzone AI aims to reduce Mean Time to Respond (MTTR) and allow analysts to focus on addressing real threats.

This repository provides a comprehensive guide to modern AI for beginners, covering both theoretical foundations and practical implementation. It emphasizes the importance of understanding both the mathematical principles and the code implementation of AI models. The repository includes resources on PyTorch, deep learning fundamentals, mathematical foundations, transformer-based LLMs, diffusion models, software engineering, and full-stack development. It also features tutorials on natural language processing with transformers, reinforcement learning, and practical deep learning for coders.

Reinforcement Learning from AI Feedback (RLAIF) is a concept that describes a type of machine learning approach where **an AI agent learns by receiving feedback or guidance from another AI system**. This concept is closely related to the field of Reinforcement Learning (RL), which is a type of machine learning where an agent learns to make a sequence of decisions in an environment to maximize a cumulative reward. In traditional RL, an agent interacts with an environment and receives feedback in the form of rewards or penalties based on the actions it takes. It learns to improve its decision-making over time to achieve its goals. In the context of Reinforcement Learning from AI Feedback, the AI agent still aims to learn optimal behavior through interactions, but **the feedback comes from another AI system rather than from the environment or human evaluators**. This can be **particularly useful in situations where it may be challenging to define clear reward functions or when it is more efficient to use another AI system to provide guidance**. The feedback from the AI system can take various forms, such as: - **Demonstrations** : The AI system provides demonstrations of desired behavior, and the learning agent tries to imitate these demonstrations. - **Comparison Data** : The AI system ranks or compares different actions taken by the learning agent, helping it to understand which actions are better or worse. - **Reward Shaping** : The AI system provides additional reward signals to guide the learning agent's behavior, supplementing the rewards from the environment. This approach is often used in scenarios where the RL agent needs to learn from **limited human or expert feedback or when the reward signal from the environment is sparse or unclear**. It can also be used to **accelerate the learning process and make RL more sample-efficient**. Reinforcement Learning from AI Feedback is an area of ongoing research and has applications in various domains, including robotics, autonomous vehicles, and game playing, among others.

AI-resources is a repository containing links to various resources for learning Artificial Intelligence. It includes video lectures, courses, tutorials, and open-source libraries related to deep learning, reinforcement learning, machine learning, and more. The repository categorizes resources for beginners, average users, and advanced users/researchers, providing a comprehensive collection of materials to enhance knowledge and skills in AI.

This repository is a compilation of excellent ML and DL tutorials created by various individuals and organizations. It covers a wide range of topics, including machine learning fundamentals, deep learning, computer vision, natural language processing, reinforcement learning, and more. The resources are organized into categories, making it easy to find the information you need. Whether you're a beginner or an experienced practitioner, you're sure to find something valuable in this repository.

Start Machine Learning in 2024 is a comprehensive guide for beginners to advance in machine learning and artificial intelligence without any prior background. The guide covers various resources such as free online courses, articles, books, and practical tips to become an expert in the field. It emphasizes self-paced learning and provides recommendations for learning paths, including videos, podcasts, and online communities. The guide also includes information on building language models and applications, practicing through Kaggle competitions, and staying updated with the latest news and developments in AI. The goal is to empower individuals with the knowledge and resources to excel in machine learning and AI.

The 'machine-learning-research' repository is a comprehensive collection of resources related to mathematics, machine learning, deep learning, artificial intelligence, data science, and various scientific fields. It includes materials such as courses, tutorials, books, podcasts, communities, online courses, papers, and dissertations. The repository covers topics ranging from fundamental math skills to advanced machine learning concepts, with a focus on applications in healthcare, genetics, computational biology, precision health, and AI in science. It serves as a valuable resource for individuals interested in learning and researching in the fields of machine learning and related disciplines.

The Machine Learning Road Map is a comprehensive guide designed to take individuals from various levels of machine learning knowledge to a basic understanding of machine learning principles using high-quality, free resources. It aims to simplify the complex and rapidly growing field of machine learning by providing a structured roadmap for learning. The guide emphasizes the importance of understanding AI for everyone, the need for patience in learning machine learning due to its complexity, and the value of learning from experts in the field. It covers five different paths to learning about machine learning, catering to consumers, aspiring AI researchers, ML engineers, developers interested in building ML applications, and companies looking to implement AI solutions.

AI By Doing is a hands-on artificial intelligence tutorial series that aims to help beginners understand the principles of machine learning and deep learning while providing practical applications. The content covers various supervised and unsupervised learning algorithms, machine learning engineering, deep learning fundamentals, frameworks like TensorFlow and PyTorch, and applications in computer vision and natural language processing. The tutorials are written in Jupyter Notebook format, combining theory, mathematical derivations, and Python code implementations to facilitate learning and understanding.

The Data Scientist Roadmap2024 provides a comprehensive guide to mastering essential tools for data science success. It includes programming languages, machine learning libraries, cloud platforms, and concepts categorized by difficulty. The roadmap covers a wide range of topics from programming languages to machine learning techniques, data visualization tools, and DevOps/MLOps tools. It also includes web development frameworks and specific concepts like supervised and unsupervised learning, NLP, deep learning, reinforcement learning, and statistics. Additionally, it delves into DevOps tools like Airflow and MLFlow, data visualization tools like Tableau and Matplotlib, and other topics such as ETL processes, optimization algorithms, and financial modeling.

InterpretML is an open-source package that incorporates state-of-the-art machine learning interpretability techniques under one roof. With this package, you can train interpretable glassbox models and explain blackbox systems. InterpretML helps you understand your model's global behavior, or understand the reasons behind individual predictions. Interpretability is essential for: - Model debugging - Why did my model make this mistake? - Feature Engineering - How can I improve my model? - Detecting fairness issues - Does my model discriminate? - Human-AI cooperation - How can I understand and trust the model's decisions? - Regulatory compliance - Does my model satisfy legal requirements? - High-risk applications - Healthcare, finance, judicial, ...

Awesome LLM Courses is a curated list of online courses focused on Large Language Models (LLMs). The repository aims to provide a comprehensive collection of free available courses covering various aspects of LLMs, including fundamentals, engineering, and applications. The courses are suitable for individuals interested in natural language processing, AI development, and machine learning. The list includes courses from reputable platforms such as Hugging Face, Udacity, DeepLearning.AI, Cohere, DataCamp, and more, offering a wide range of topics from pretraining LLMs to building AI applications with LLMs. Whether you are a beginner looking to understand the basics of LLMs or an intermediate developer interested in advanced topics like prompt engineering and generative AI, this repository has something for everyone.

Pgx is a collection of GPU/TPU-accelerated parallel game simulators for reinforcement learning (RL). It provides JAX-native game simulators for various games like Backgammon, Chess, Shogi, and Go, offering super fast parallel execution on accelerators and beautiful visualization in SVG format. Pgx focuses on faster implementations while also being sufficiently general, allowing environments to be converted to the AEC API of PettingZoo for running Pgx environments through the PettingZoo API.

This repository contains the reinforcement learning training code for Trackmania AI with Reinforcement Learning. It is a research work-in-progress project that aims to apply reinforcement learning principles to play Trackmania. The code is constantly evolving and may not be clean or easily usable. The training hyperparameters are intentionally changed in the public repository to encourage understanding of reinforcement learning principles. The project may not receive active support for setup or usage at the moment.

This repository, Online RLHF, focuses on aligning large language models (LLMs) through online iterative Reinforcement Learning from Human Feedback (RLHF). It aims to bridge the gap in existing open-source RLHF projects by providing a detailed recipe for online iterative RLHF. The workflow presented here has shown to outperform offline counterparts in recent LLM literature, achieving comparable or better results than LLaMA3-8B-instruct using only open-source data. The repository includes model releases for SFT, Reward model, and RLHF model, along with installation instructions for both inference and training environments. Users can follow step-by-step guidance for supervised fine-tuning, reward modeling, data generation, data annotation, and training, ultimately enabling iterative training to run automatically.

Humanoid-Gym is a reinforcement learning framework designed for training locomotion skills for humanoid robots, focusing on zero-shot transfer from simulation to real-world environments. It integrates a sim-to-sim framework from Isaac Gym to Mujoco for verifying trained policies in different physical simulations. The codebase is verified with RobotEra's XBot-S and XBot-L humanoid robots. It offers comprehensive training guidelines, step-by-step configuration instructions, and execution scripts for easy deployment. The sim2sim support allows transferring trained policies to accurate simulated environments. The upcoming features include Denoising World Model Learning and Dexterous Hand Manipulation. Installation and usage guides are provided along with examples for training PPO policies and sim-to-sim transformations. The code structure includes environment and configuration files, with instructions on adding new environments. Troubleshooting tips are provided for common issues, along with a citation and acknowledgment section.

TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. It 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.

Scikit-decide is an AI framework for Reinforcement Learning, Automated Planning and Scheduling. It provides a unified interface to define and solve decision-making problems, making it easy to switch between different algorithms and domains.

OpenRL is an open-source general reinforcement learning research framework that supports training for various tasks such as single-agent, multi-agent, offline RL, self-play, and natural language. Developed based on PyTorch, the goal of OpenRL is to provide a simple-to-use, flexible, efficient and sustainable platform for the reinforcement learning research community. It supports a universal interface for all tasks/environments, single-agent and multi-agent tasks, offline RL training with expert dataset, self-play training, reinforcement learning training for natural language tasks, DeepSpeed, Arena for evaluation, importing models and datasets from Hugging Face, user-defined environments, models, and datasets, gymnasium environments, callbacks, visualization tools, unit testing, and code coverage testing. It also supports various algorithms like PPO, DQN, SAC, and environments like Gymnasium, MuJoCo, Atari, and more.

TFTMuZeroAgent is an implementation of a purely artificial intelligence algorithm to play Teamfight Tactics, an auto chess game made by Riot. It uses a simulation of TFT Set 4 and the MuZero reinforcement learning algorithm. The project provides a multi-agent petting zoo environment where players, pool, and game round classes are designed for AI project. The implementation excludes graphics and sounds but covers all aspects of the game from set 4. The codebase is open for contributions and improvements, allowing for additional models to be added to the environment.

Linesight is a reinforcement learning project focused on advancing AI capabilities in the racing game Trackmania. It aims to push the boundaries of AI performance by utilizing deep learning algorithms to achieve human-level driving and beat world records on official campaign tracks. The project provides an interface to interact with Trackmania Nations Forever programmatically, enabling tasks such as sending inputs, retrieving car states, and capturing screenshots. With a strong emphasis on equality of input devices, Linesight serves as a benchmark for testing various reinforcement learning algorithms in a challenging and dynamic gaming environment.

Learn & code RL with me!

Summarizes, explains, and guides through RL and Spinning Up.

快速入门强化学习的知识咀嚼器

Your go-to coach for career growth 👨👩🎓 Practice questions in multiple languages: The AI can help you build confidence for your next interview by providing you with positive reinforcement and feedback.

A GPT that compliments users based on their photos.