OpenAI OpenAI gym AI technology Top Builders

Explore the top contributors showcasing the highest number of OpenAI OpenAI gym AI technology app submissions within our community.

OpenAI gym

Gym is a toolkit from OpenAI that offers a wide array of simulated environments (e.g. Atari games, board games, 2D and 3D physical simulations) for you to train agents, benchmark them, and create new Reinforcement Learning algorithms.

General
Relese dateApril 27, 2016
AuthorOpenAI
Repositoryhttps://github.com/openai/gym
TypeReinforcement Learning

Libraries

A curated list of libraries and technologies to help you play with OpenAI Gym.

  • Gym Library Gym is a standard API for reinforcement learning, and a diverse collection of reference environments
  • OpenAI Gym repository Gym is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. Since its release, Gym's API has become the field standard for doing this.
  • Gymnasium Gymnasium is an open source Python library for developing and comparing reinforcement learning algorithms by providing a standard API to communicate between learning algorithms and environments, as well as a standard set of environments compliant with that API. This is a fork of OpenAI's Gym library

OpenAI OpenAI gym AI technology Hackathon projects

Discover innovative solutions crafted with OpenAI OpenAI gym AI technology, developed by our community members during our engaging hackathons.

Falcon Songbird

Falcon Songbird

Resume building can be time-consuming and ineffective if not done correctly. Getting resumes through the ATS (Applicant Tracking System) requires a different approach, which the Falcon Song-Bird application addresses. Our application starts with an introduction to Falcon Songbird, providing an overview of its features and benefits. Next, the interactive Manual Resume Assistant helps you upload your resume and job description, create a summary, define job roles, identify keywords for technical skills, and update work history, education, and certifications. It also identifies missing skills for training purposes, writes a cover letter, and collects all materials for download. This system allows you to fine-tune a configuration file of algorithms for the next phase. The Automatic Resume Builder uses this configuration file to generate a polished resume tailored to your needs. The Course Creator develops a zero-to-hero course with 10 hands-on exercises to help you upgrade your skills. Finally, the Scoring Utility evaluates how well your resume matches the job description, providing a score, that highlights strengths and areas for improvement. From a business perspective, Falcon Songbird offers several benefits: Increased Efficiency: Automating the resume-building process saves time, allowing job seekers to focus more on their job search. Higher Success Rates: Advanced algorithms tailored to pass ATS filters increase the likelihood of your resume being seen by hiring managers. Skill Enhancement: The Course Creator identifies skill gaps and provides a structured learning path to bridge them. Customization: The Manual Resume Assistant and Automatic Resume Builder ensure unique and accurate resumes. Objective Evaluation: The Scoring Utility gives actionable insights to improve your resume. Overall, Falcon Songbird streamlines the resume-building process, increasing your chances of landing your desired job while enhancing your professional skills.

Basic Social App

Basic Social App

Basic Social App Finally a basic social media app basic.social | basicsocial.app | basicsocial.ai Introduction: Basic Social App is a simple social media platform designed for effortless user engagement and connectivity. Unlike traditional social media applications, it emphasizes simplicity in user on-boarding and interaction, allowing users to quickly share their thoughts and connect with others through AI-driven recommendations. How it works: Step 1: User on-boarding User can easily on-board with just phone number or email. We allow users to follow otp based login. Step 2: Whatโ€™s on mind User is prompted write, whatโ€™s on mind. He can also select few AI generated prompts as well. Step 3: Recommendations AI powered recommendations. AI understand the user interaction and then starts recommending the best content, people, services, apps, subscriptions. Step 4: Creators Users can create content, reels, text, audios, ads, news etc and rather than spending lot of time in hastags or other meta data. They can simple specify whom to show. Step 5: Organizations | Marketers Organizations rather than spending lot of money on marketing to large unclear audience, With Basic.Social they can clearly reach their potential clients immediately. Hence a basic social media app. Features: AI Recommendation Chat Voice Call Video Call Feed Analytics Content creation tools Unique Selling Points (USP) Effortless On-boarding: Users can join the platform using just their phone number or email with OTP authentication, eliminating the need for extensive profile creation. AI-Powered Connectivity: AI algorithms help users connect with millions of other users with similar interests and thoughts, enhancing engagement and community building. Marketing Innovation: Marketers and organizations can reach users by offering freebies, fostering a more organic and engaging form of advertisement. Minimalist Design: The app focuses on simplicity and usability.

Adapt-a-RAG

Adapt-a-RAG

Introduction Adapt-a-RAG is an innovative application that leverages the power of retrieval augmented generation to provide accurate and relevant answers to user queries. By adapting itself to each query, Adapt-a-RAG ensures that the generated responses are tailored to the specific needs of the user. The application utilizes various data sources, including documents, GitHub repositories, and websites, to gather information and generate synthetic data. This synthetic data is then used to optimize the prompts of the Adapt-a-RAG application, enabling it to provide more accurate and contextually relevant answers. How It Works Adapt-a-RAG works by following these key steps: Data Collection: The application collects data from various sources, including documents, GitHub repositories, and websites. It utilizes different reader classes such as CSVReader, DocxReader, PDFReader, ChromaReader, and SimpleWebPageReader to extract information from these sources. Synthetic Data Generation: Adapt-a-RAG generates synthetic data using the collected data. It employs techniques such as data augmentation and synthesis to create additional training examples that can help improve the performance of the application. Prompt Optimization: The synthetic data is used to optimize the prompts of the Adapt-a-RAG application. By fine-tuning the prompts based on the generated data, the application can generate more accurate and relevant responses to user queries. Recompilation: Adapt-a-RAG recompiles itself every run based on the optimized prompts and the specific user query. This dynamic recompilation allows the application to adapt and provide tailored responses to each query. Question Answering: Once recompiled, Adapt-a-RAG takes the user query and retrieves relevant information from the collected data sources. It then generates a response using the optimized prompts and the retrieved information, providing accurate and contextually relevant answers to the user.