Reinforcement Learning Applications

Browse applications built on Reinforcement Learning technology. Explore PoC and MVP applications created by our community and discover innovative use cases for Reinforcement Learning technology.

WebML Assist

Elevate the realm of machine learning with "WebML Assist." This innovative project integrates the power of WebGPU and the capabilities of the "BabyAGI" framework to offer a seamless, high-speed experience in machine learning tasks. "WebML Assist" empowers users to build, train, and deploy AI models effortlessly, leveraging the parallel processing of GPUs for accelerated training. The platform intuitively guides users through data preprocessing, model architecture selection, and hyperparameter tuning, all while harnessing the performance boost of WebGPU. Experience the future of efficient and rapid machine learning with "WebML Assist." Technologies Used: WebGPU OpenAI APIs (GPT-3.5, GPT-4) BabyAGI Pinecone API (for task management) FineTuner.ai (for no-code AI components) Python (for backend) Redis (for data caching) Qdrant (for efficient vector similarity search) Generative Agents (for simulating human behavior). AWS SageMaker (for developing machine learning models quickly and easily build, train, and deploy). Reinforcement learning (is an area of machine learning concerned with how intelligent agents). Categories: Machine Learning AI-Assisted Task Management Benefits: "WebML Assist" brings together the capabilities of WebGPU and AI frameworks like "BabyAGI" to provide an all-encompassing solution for ML enthusiasts. Users can seamlessly transition from data preprocessing to model deployment while harnessing the GPU's power for faster training. The incorporation of AI agents ensures intelligent suggestions and efficient task management. By integrating AI, GPU acceleration, and user-friendly interfaces, "WebML Assist" empowers both novice and experienced ML practitioners to unlock the true potential of their projects, transforming the way AI models are built, trained, and deployed.

GPU Titans
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WebGPUOpenAIBabyAGIFineTuner.aiGPT-3.5RedisQdrantGenerative AgentsAWS SageMakerReinforcement Learning

Trading-Agent-

A trading agent AI is an artificial intelligence system that uses computational intelligence methods such as machine learning and deep reinforcement learning to automatically discover, implement, and fine-tune strategies for autonomous adaptive automated trading in financial markets This project implements a Stock Trading Bot, trained using Deep Reinforcement Learning, specifically Deep Q-learning. Implementation is kept simple and as close as possible to the algorithm discussed in the paper, for learning purposes. Generally, Reinforcement Learning is a family of machine learning techniques that allow us to create intelligent agents that learn from the environment by interacting with it, as they learn an optimal policy by trial and error. This is especially useful in many real world tasks where supervised learning might not be the best approach due to various reasons like nature of task itself, lack of appropriate labelled data, etc. The important idea here is that this technique can be applied to any real world task that can be described loosely as a Markovian process. This work uses a Model-free Reinforcement Learning technique called Deep Q-Learning (neural variant of Q-Learning). At any given time (episode), an agent abserves it's current state (n-day window stock price representation), selects and performs an action (buy/sell/hold), observes a subsequent state, receives some reward signal (difference in portfolio position) and lastly adjusts it's parameters based on the gradient of the loss computed. There have been several improvements to the Q-learning algorithm over the years, and a few have been implemented in this project: Vanilla DQN DQN with fixed target distribution Double DQN Prioritized Experience Replay Dueling Network Architectures Trained on GOOG 2010-17 stock data, tested on 2019 with a profit of $1141.45 (validated on 2018 with profit of $863.41):

Cyber World
Reinforcement LearningprivateGPTgpt4allChatGPT

Daila a Dynamic Learning App

Daila is an innovative and revolutionary app that has the potential to transform the way people learn. It is a personalized learning app that leverages the power of GPT-3.5, a state-of-the-art language processing AI model, and AWS services to provide a customized and tailored learning experience to students and learners of all kinds. With Daila, users can expect to receive a uniquely curated learning experience that is based on their individual learning styles, interests, and goals. What sets Daila apart from other learning apps is its ability to adapt and evolve based on user feedback and performance. The app is designed to provide users with tailored study materials and exercises while analyzing their progress to dynamically modify and optimize their study path. This means that users will receive a truly personalized and adaptive learning experience that is unmatched in the industry. Furthermore, Daila has plans to expand its content library and partner with various educational providers and institutions to offer a wider range of courses and study materials for users. Additionally, the app plans to monetize through a subscription model and affiliate marketing, recommending courses from external sources such as Coursera and other educational platforms. In summary, Daila is a powerful and innovative app that has the potential to revolutionize the way we learn. With its personalized approach and cutting-edge technology, Daila offers a truly unique and customized learning experience that is designed to meet the needs of each individual user. With its plans for expansion and monetization, Daila is poised to become a leading player in the education technology industry.

Beta Builders
RedisReinforcement LearningChatGPTGPT-3

Hyperbot

Hyperbot is an AI-powered chatbot developed by ChatGPT, which has beenconnected to models such as DallE and embeddings to provide various services. Some of the services that Hyperbot can provide are: 1. Coding-related queries: Hyperbot can answer questions related to coding and programming languages such as Python, Java, C++, and more. It can also provide solutions to common coding-related problems. 2. Generate art: Thanks to its integration with DallE, Hyperbot can generate art based on user requests. It can create various types of art and visualizations, including drawings, sketches, and more. 3. Real-time Current Affairs and News: It can fetch the latest news and updates from various sources, including national and international news, sports, entertainment, politics, and more. 4. Weather updates and forecasts: Hyperbot can provide real-time weather updates and forecasts for the user's location or any other location. 5. Create or compose tweets or LinkedIn posts/emails: Hyperbot can help the user create tweets, LinkedIn posts or emails with ease. It can even suggest content and ideas for the message. 6. Play your favorite song or YouTube video: If the user requests it, Hyperbot can play their favorite song, video or recommend any other content based on the user's preferences. In a nutshell, Hyperbot is a multi-functional chatbot capable of providing a wide range of services, making it a useful tool for various needs. soon it will have feature wherein you can upload any document and it answers to that as well

team phoeniks
ChatGPTCodexDALL-E-2OpenAI gymReinforcement LearningLangChain

The Future of AI podcast

An artificial intelligence podcast that is written by ChatGPT, GPT-3.5, Open-AI davinci, and human assistance. The art is generated by Stable Diffusion, Open Journey, and Dall-E 2. It is read by Natural Readers text-to-speech and Lifelike Speech Synthesis Google Cloud. The platform used is Anchor.fm and the availability of the podcast are in Google Podcasts, Apple Podcasts, Amazon Music, Spotify, Castbox, Pocket Casts, RadioPublic, and Stitcher. The podcast description is: "Join us as we explore the rapidly advancing world of artificial intelligence, and what it means for our future. In each episode, we'll discuss the latest AI research and developments, and how they are poised to impact various industries and aspects of our daily lives. From self-driving cars to intelligent virtual assistants, we'll delve into the potential and the challenges of this rapidly evolving technology. Tune in to stay up-to-date on the future of AI and its impact on society." Created and written by Artificial Intelligences and Cyber World. Currently the podcast has 12 episode in season 1 which has one episode for introduction and special and it has 5 episode currently for season 2. AI has come a long way since its inception and has been widely used in various fields such as healthcare, finance, and transportation. AI-powered machines and systems have the ability to learn and adapt to new situations without the need for human intervention. This ability of AI has made it an integral part of various industries and has brought about significant changes in the way we work and live. The current state of the AI industry is quite promising. The AI market is expected to grow from $9.5 billion in 2018 to $118.6 billion by 2025. The adoption of AI is increasing at a rapid pace and is being used in a variety of applications such as image recognition, speech recognition, and natural language processing. The use of AI in healthcare has also shown promising results, with AI-powered systems.

The Future of AI
OpenAI gymChatGPTReinforcement LearningStable DiffusionRedisCohere Generate