Search Results for "decision" - Page 2
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Showing 134 open source projects for "decision"
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DreamerV3
Mastering Diverse Domains through World Models
...The system works by building an internal model of the environment and then using that model to simulate possible future outcomes of actions, allowing the agent to learn from imagined experiences rather than only from real interactions. This approach enables the algorithm to efficiently learn policies for decision-making tasks that would otherwise require enormous amounts of data or computational resources. DreamerV3 was designed as a general reinforcement learning framework that can solve diverse tasks using the same configuration of hyperparameters across many environments. In research demonstrations, the algorithm has been shown to perform strongly across more than one hundred control tasks and complex simulated environments. -
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LLM Colosseum
Benchmark LLMs by fighting in Street Fighter 3
...The system places language models inside the environment of the classic video game Street Fighter III, where they must interpret the game state and decide which actions to perform during combat. This setup creates a dynamic environment that tests reasoning, situational awareness, and decision-making abilities in real time. Instead of relying purely on reward signals as in reinforcement learning agents, the models analyze contextual information and generate strategic actions based on the game environment. Performance is evaluated using a competitive ranking system that assigns models an ELO rating based on their results across matches against other models. -
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AgentBench
A Comprehensive Benchmark to Evaluate LLMs as Agents (ICLR'24)
...Unlike traditional language model benchmarks that focus on static text tasks, AgentBench measures how models perform in interactive environments that require planning, reasoning, and decision-making. The benchmark includes multiple environments that simulate realistic scenarios such as web interaction, database querying, and problem solving tasks. These environments require agents to interpret instructions, take actions, and adapt their strategies based on feedback from the environment. AgentBench also includes an evaluation framework that measures success rates, rewards, and task completion performance across different agent implementations. ... -
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MiroFish
A Simple and Universal Swarm Intelligence Engine
...Users can inject variables or conditions into this simulated environment from a “god’s eye view,” enabling iterative prediction of future trends under different assumptions, which can be useful for decision support, scenario planning, or creative exploration. The engine includes both backend and frontend components, with configuration and deployment instructions for local and containerized setups, and is designed to produce detailed predictive reports based on interactions and emergent patterns within the simulated world. -
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Letta
Letta (formerly MemGPT) is a framework for creating LLM services
Letta is an AI-powered task automation framework designed to handle workflow automation, natural language commands, and AI-driven decision-making. -
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Orange Data Mining
Orange: Interactive data analysis
...Build data analysis workflows visually, with a large, diverse toolbox. Perform simple data analysis with clever data visualization. Explore statistical distributions, box plots and scatter plots, or dive deeper with decision trees, hierarchical clustering, heatmaps, MDS and linear projections. Even your multidimensional data can become sensible in 2D, especially with clever attribute ranking and selections. Interactive data exploration for rapid qualitative analysis with clean visualizations. Graphic user interface allows you to focus on exploratory data analysis instead of coding, while clever defaults make fast prototyping of a data analysis workflow extremely easy. ... -
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Homemade Machine Learning
Python examples of popular machine learning algorithms
...Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is pedagogical: you’ll see linear regression, logistic regression, k-means clustering, neural nets, decision trees, etc., built in Python using fundamentals like NumPy and Matplotlib, not hidden behind API calls. It is well suited for learners who want to move beyond library usage to understand how algorithms operate internally—how cost functions, gradients, updates and predictions work. -
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River ML
Online machine learning in Python
River is a Python library for online machine learning. It aims to be the most user-friendly library for doing machine learning on streaming data. River is the result of a merger between creme and scikit-multiflow. -
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Cybergod
A program that can do anything to earn money without human operators
AGI Computer Control is an experimental autonomous software system designed to operate independently and generate income without human intervention. It aims to simulate artificial general intelligence (AGI) by leveraging evolutionary algorithms, deep active inference, and other advanced AI techniques. The project explores the boundaries of machine autonomy and self-directed behavior in computational environments. -
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Think Bayes 2
Text and code for the second edition of Think Bayes, by Allen Downey
...Separate solution materials help learners check their work and explore alternative approaches. The lessons cover probability distributions, Bayesian updating, estimation, prediction, comparison, and decision-making. Notebooks can run in Google Colab or be downloaded for local use. The repository also contains book sources, supporting code, and environment files for reproducible study. -
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SGR Agent Core
Schema-Guided Reasoning (SGR) has agentic system design
SGR Agent Core is an open-source framework for building intelligent AI research agents based on a methodology known as Schema-Guided Reasoning (SGR). The framework provides a core library that allows developers to design autonomous agents capable of structured reasoning and complex task execution. Instead of relying solely on free-form prompts, the system organizes reasoning processes around schemas that guide how agents analyze problems, gather information, and generate outputs. This... -
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DI-engine
OpenDILab Decision AI Engine
DI-engine is a unified reinforcement learning (RL) platform for reproducible and scalable RL research. It offers modular pipelines for various RL algorithms, with an emphasis on production-level training and evaluation. -
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Dynamiq
An orchestration framework for agentic AI and LLM applications
...The framework supports the creation of multi-agent systems where different AI agents collaborate to solve tasks such as information retrieval, document analysis, or automated decision making. Dynamiq also includes built-in support for retrieval-augmented generation pipelines that allow models to access external documents and knowledge bases during inference. -
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OpenKore
A free/open source client and automation tool for Ragnarok Online
...With a strong community and extensive documentation, it is widely used by players and developers who want to explore automation, bot behavior, or server testing. The software emphasizes configurability, giving users control over character strategies and in-game decision-making. -
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Claude Code Plugins
Intelligent automation and multi-agent orchestration for Claude Code
...It emphasizes simplicity and composability, allowing developers to define agent behaviors through reusable components rather than monolithic logic. The framework supports integration with various tools and APIs, enabling agents to perform actions such as data retrieval, automation, and decision-making processes. It is particularly useful for experimenting with autonomous or semi-autonomous systems that rely on prompt-driven logic and tool usage. The design encourages transparency and control over how agents operate, making it suitable for both prototyping and production scenarios. -
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Sherloq
An open source digital image forensic toolset
Sherloq is a research-oriented toolkit designed for digital image forensics, providing an integrated environment to experiment with algorithms for image analysis and tampering detection. Rather than functioning as an automated decision-making system, it serves as a companion tool for researchers, enthusiasts, and students who want to explore forensic techniques from scientific literature and workshops. The project emphasizes transparency and community collaboration, contrasting with proprietary forensic tools that often rely on secrecy. Initially developed in C++ in 2015 and later transitioned to a Qt-based GUI in 2017, Sherloq has since been ported to Python with PySide2, Matplotlib, and OpenCV to improve accessibility and ease of development. ... -
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PyMC
Bayesian Modeling and Probabilistic Programming in Python
...Built on top of computational tools like Aesara and NumPy, PyMC allows users to define models using intuitive syntax and perform inference using MCMC, variational inference, and other advanced algorithms. It’s widely used in scientific research, data science, and decision modeling. -
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TextWorld
TextWorld is a sandbox learning environment for the training
...Developed by Microsoft Research, TextWorld focuses on language understanding, planning, and interaction in complex, narrative-driven environments. It generates games procedurally, enabling scalable testing of agents’ natural language processing and decision-making abilities. -
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Alpamayo 1
Bridging Reasoning and Action Prediction
...It incorporates vision-language-action modeling, enabling it to process sensor data and contextual information simultaneously. Alpamayo supports tasks such as trajectory prediction, auto-labeling, and reasoning-based decision making. The system is optimized for high-performance GPU environments and is intended primarily for experimentation and benchmarking. Overall, it represents an advanced step toward integrating reasoning into autonomous driving pipelines. -
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Agentic RAG for Dummies
A modular Agentic RAG built with LangGraph
...The project explains the principles behind agentic retrieval pipelines where language models can dynamically decide when to retrieve information, analyze results, and plan further actions. Instead of relying on static retrieval pipelines, the system shows how agents can orchestrate retrieval, reasoning, and tool usage in a more flexible decision loop. The repository provides practical examples and tutorials that guide developers through building agentic RAG systems using modern AI frameworks. These examples illustrate how agents can access knowledge bases, retrieve documents, analyze them, and refine their queries during multi-step reasoning processes. The repository focuses on simplifying complex architectural concepts so that beginners can understand how agentic retrieval systems are constructed. -
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autoresearch for AMD
AI agents running research on single-GPU nanochat training
...The system is built around a minimal structure that includes a data preparation module, a training script that can be modified, and a program specification that guides the agent’s decision-making process. During each iteration, the agent edits the training code, runs an experiment within a fixed time budget, evaluates performance metrics, and decides whether to retain or discard the changes. This loop allows the system to explore a wide range of architectural and hyperparameter configurations without human intervention. ... -
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Dendrite
Tools to build web AI agents that can authenticate
Dendrite Python SDK is a toolkit for building web AI agents that can authenticate, interact with, and extract data from any website, facilitating web automation tasks. -
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autoMate
AI tool for automating desktop tasks via natural language input
autoMate is an AI-powered local automation tool designed to enable users to control and automate their computers using natural language instructions instead of traditional scripting or rule-based systems. It combines large language models with computer vision techniques to interpret user intent and understand on-screen content, allowing it to interact with graphical interfaces similarly to a human user. autoMate follows an observe-decide-act workflow, where it analyzes the screen, plans... -
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imodelsX
Interpretable prompting and models for NLP
...Generates a prompt that explains patterns in data (Official) Explain the difference between two distributions. Find a natural-language prompt using input-gradients. Fit a better linear model using an LLM to extract embeddings. Fit better decision trees using an LLM to expand features. Finetune a single linear layer on top of LLM embeddings. Use these just a like a sci-kit-learn model. During training, they fit better features via LLMs, but at test-time, they are extremely fast and completely transparent. -
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GLM-4.6
Agentic, Reasoning, and Coding (ARC) foundation models
GLM-4.6 is the latest iteration of Zhipu AI’s foundation model, delivering significant advancements over GLM-4.5. It introduces an extended 200K token context window, enabling more sophisticated long-context reasoning and agentic workflows. The model achieves superior coding performance, excelling in benchmarks and practical coding assistants such as Claude Code, Cline, Roo Code, and Kilo Code. Its reasoning capabilities have been strengthened, including improved tool usage during inference...
