Best AI tools for< Reinforcement Learning >

The Farama Foundation is a platform dedicated to maintaining and supporting the world's open-source reinforcement learning tools. With a large community of contributors and a vast number of installations, the foundation plays a crucial role in advancing the field of AI. They offer a range of tools and resources for developers and researchers interested in reinforcement learning.

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.

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.

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.

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.

Ferhat Erata is an AI application developed by a Computer Science PhD graduate from Yale University. The application focuses on training transformers to solve NP-complete problems using reinforcement learning and improving test-time compute strategies for reasoning. It also explores learning randomized reductions and program properties for security, privacy, and side-channel resilience. Ferhat Erata is currently an Applied Scientist at the Automated Reasoning Group at AWS, working on Neuro-Symbolic AI to prevent factual errors caused by LLM hallucinations using mathematically sound Automated Reasoning checks.

John Schulman's Homepage is an AI tool developed by a researcher at Anthropic. The website focuses on aligning large language models, scalable oversight, and developing better written specifications of model behavior. It showcases the researcher's work in the field of AI, including projects like ChatGPT and the OpenAI API. The homepage also highlights the researcher's academic background, including a PhD in Computer Science from UC Berkeley with a focus on robotics and reinforcement learning.

The website openai.com is an AI tool that provides cutting-edge artificial intelligence solutions. It offers a wide range of AI applications and services to enhance various industries and sectors. OpenAI is known for its advanced AI models and research in natural language processing, reinforcement learning, and more. The platform aims to democratize AI and make it accessible to developers, researchers, and businesses worldwide.

The Center for Human-Compatible Artificial Intelligence (CHAI) is dedicated to building exceptional AI systems for the benefit of humanity. Their mission is to steer AI research towards developing systems that are provably beneficial. CHAI collaborates with researchers, faculty, staff, and students to advance the field of AI alignment and care-like relationships in machine caregiving. They focus on topics such as political neutrality in AI, offline reinforcement learning, and coordination with experts.

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.

DeepSeek R1 is a revolutionary open-source AI model for advanced reasoning that outperforms leading AI models in mathematics, coding, and general reasoning tasks. It utilizes a sophisticated MoE architecture with 37B active/671B total parameters and 128K context length, incorporating advanced reinforcement learning techniques. DeepSeek R1 offers multiple variants and distilled models optimized for complex problem-solving, multilingual understanding, and production-grade code generation. It provides cost-effective pricing compared to competitors like OpenAI o1, making it an attractive choice for developers and enterprises.

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.

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.

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.

Salesably is an AI-driven sales practice platform that transforms sales training from static to dynamic through interactive playbooks, AI-powered practice, skills certification, and performance analytics. It offers sales trainers and managers a cutting-edge platform to enhance programs, drive better results, and create new revenue streams. With features like practice against diverse personas, learning paths & certification, customizable platform, measurable impact, objection handling mastery, discovery questioning excellence, pitch optimization workshops, and actionable insights for sales leaders and trainers, Salesably empowers sales teams to become masters of buyer-centric selling.

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.

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.

Pearl is a production-ready Reinforcement Learning AI agent library open-sourced by the Applied Reinforcement Learning team at Meta. It enables researchers and practitioners to develop Reinforcement Learning AI agents that prioritize cumulative long-term feedback over immediate feedback and can adapt to environments with limited observability, sparse feedback, and high stochasticity. Pearl offers a diverse set of unique features for production environments, including dynamic action spaces, offline learning, intelligent neural exploration, safe decision making, history summarization, and data augmentation.

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.

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.

REINVENT is a molecular design tool for de novo design, scaffold hopping, R-group replacement, linker design, molecule optimization, and other small molecule design tasks. It uses a Reinforcement Learning (RL) algorithm to generate optimized molecules compliant with a user-defined property profile defined as a multi-component score. Transfer Learning (TL) can be used to create or pre-train a model that generates molecules closer to a set of input molecules.

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.

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.

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.

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.

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.

YGO Agent is a project focused on using deep learning to master the Yu-Gi-Oh! trading card game. It utilizes reinforcement learning and large language models to develop advanced AI agents that aim to surpass human expert play. The project provides a platform for researchers and players to explore AI in complex, strategic game environments.

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.

RLHF Book is a work-in-progress textbook covering the fundamentals of Reinforcement Learning from Human Feedback (RLHF). It is built on the Pandoc book template and is meant for people with a basic ML and/or software background. The content for the book is licensed under the Creative Commons Non-Commercial Attribution License, CC BY-NC 4.0. The repository contains a simple template for building Pandoc documents, allowing users to compile markdown files into readable files such as PDF, EPUB, and HTML.

veRL is a flexible and efficient reinforcement learning training framework designed for large language models (LLMs). It allows easy extension of diverse RL algorithms, seamless integration with existing LLM infrastructures, and flexible device mapping. The framework achieves state-of-the-art throughput and efficient actor model resharding with 3D-HybridEngine. It supports popular HuggingFace models and is suitable for users working with PyTorch FSDP, Megatron-LM, and vLLM backends.

Craftium is an open-source platform based on the Minetest voxel game engine and the Gymnasium and PettingZoo APIs, designed for creating fast, rich, and diverse single and multi-agent environments. It allows for connecting to Craftium's Python process, executing actions as keyboard and mouse controls, extending the Lua API for creating RL environments and tasks, and supporting client/server synchronization for slow agents. Craftium is fully extensible, extensively documented, modern RL API compatible, fully open source, and eliminates the need for Java. It offers a variety of environments for research and development in reinforcement learning.

This repository is a collection of research notes and resources focusing on training large language models (LLMs) and Reinforcement Learning from Human Feedback (RLHF). It includes methodologies, techniques, and state-of-the-art approaches for optimizing preferences and model alignment in LLM training. The purpose is to serve as a reference for researchers and engineers interested in reinforcement learning, large language models, model alignment, and alternative RL-based methods.

OREAL is a reinforcement learning framework designed for mathematical reasoning tasks, aiming to achieve optimal performance through outcome reward-based learning. The framework utilizes behavior cloning, reshaping rewards, and token-level reward models to address challenges in sparse rewards and partial correctness. OREAL has achieved significant results, with a 7B model reaching 94.0 pass@1 accuracy on MATH-500 and surpassing previous 32B models. The tool provides training tutorials and Hugging Face model repositories for easy access and implementation.

FinRL-DeepSeek is a project focusing on LLM-infused risk-sensitive reinforcement learning for trading agents. It provides a framework for training and evaluating trading agents in different market conditions using deep reinforcement learning techniques. The project integrates sentiment analysis and risk assessment to enhance trading strategies in both bull and bear markets. Users can preprocess financial news data, add LLM signals, and train agent-ready datasets for PPO and CPPO algorithms. The project offers specific training and evaluation environments for different agent configurations, along with detailed instructions for installation and usage.

This repository is dedicated to enhancing Language Model (LLM) reasoning with reinforcement learning (RL). It includes a collection of the latest papers, slides, and materials related to RL-based LLM reasoning, aiming to facilitate quick learning and understanding in this field. Starring this repository allows users to stay updated and engaged with the forefront of RL-based LLM reasoning.

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