Search Results for "agent" - Page 18
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Showing 497 open source projects for "agent"
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Atgen A2 Automation
Atgen A2 is a radically simple Workload Automation & Scheduling tool.
Atgen A2 is a radically simple IT automation solution that automates application builds, continuous testing, data parsing & processing, report generation, batch processing, job scheduling, and many other IT needs. Avoid running repetitive batch jobs and managing access to deploy and update your applications — automate in a language that approaches plain English, using SSH or WinRM, with no agents to install on remote systems. https://www.atgensoft.com/ You can install a released version... -
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Datadog Agent
Datadog Agent Version 5
Datadog Agent is an open-source agent for collecting metrics, logs, and traces from servers, applications, and services, sending data to Datadog for analysis. -
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googler
Google Search, Google Site Search, Google News from the terminal
googler is a power tool to Google (Web & News) and Google Site Search from the command-line. It shows the title, URL and abstract for each result, which can be directly opened in a browser from the terminal. Results are fetched in pages (with page navigation). Supports sequential searches in a single googler instance. googler was initially written to cater to headless servers without X. You can integrate it with a text-based browser. However, it has grown into a very handy and flexible... -
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phpsploit
Full-featured C2 framework which silently persists on webserver
...Session saving/loading feature & persistent history. Multi-request support for large payloads (such as uploads) Provides a powerful, highly configurable settings engine. Each setting, such as user-agent has a polymorphic mode. Customizable environment variables for plugin interaction. Provides a complete plugin development API. -
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MADDPG
Code for the MADDPG algorithm from a paper
MADDPG (Multi-Agent Deep Deterministic Policy Gradient) is the official code release from OpenAI’s paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The repository implements a multi-agent reinforcement learning algorithm that extends DDPG to scenarios where multiple agents interact in shared environments. Each agent has its own policy, but training uses centralized critics conditioned on the observations and actions of all agents, enabling learning in cooperative, competitive, and mixed settings. ... -
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End-to-End Negotiator
Deal or No Deal? End-to-End Learning for Negotiation Dialogues
End-to-End Negotiator is a PyTorch-based research framework developed by Facebook AI Research to train neural agents capable of conducting strategic negotiations in natural language. The project implements the models presented in two key papers: “Deal or No Deal? End-to-End Learning for Negotiation Dialogues” and “Hierarchical Text Generation and Planning for Strategic Dialogue”. It enables agents to plan, reason, and communicate effectively to maximize outcomes in multi-turn negotiations... -
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moosha-ai
An intelligent virtual assistant (IVA) or intelligent personal assista
An intelligent virtual assistant (IVA) or intelligent personal assistant (IPA) is a software agent that can perform tasks or services for an individual based on commands or questions. Sometimes the term "chatbot" is used to refer to virtual assistants generally or specifically accessed by online chat. In some cases, online chat programs are exclusively for entertainment purposes. Some virtual assistants are able to interpret human speech and respond via synthesized voices. -
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SMAC
SMAC: The StarCraft Multi-Agent Challenge
SMAC (StarCraft II Multi-Agent Challenge) is a benchmark environment for cooperative multi-agent reinforcement learning (MARL), based on real-time strategy (RTS) game scenarios in StarCraft II. It allows researchers to test algorithms where multiple units (agents) must collaborate to win battles against built-in game AI opponents. SMAC provides a controlled testbed for studying decentralized execution and centralized training paradigms in MARL. -
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deep-q-learning
Minimal Deep Q Learning (DQN & DDQN) implementations in Keras
The deep-q-learning repository authored by keon provides a Python-based implementation of the Deep Q-Learning algorithm — a cornerstone method in reinforcement learning. It implements the core logic needed to train an agent using Q-learning with neural networks (i.e. approximating Q-values via deep nets), setting up environment interaction loops, experience replay, network updates, and policy behavior. For learners and researchers interested in reinforcement learning, this repo offers a concrete, runnable example bridging theory and practice: you can execute the code, play with hyperparameters, observe convergence behavior, and see how deep Q-learning learns policies over time in standard environments. ... -
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Neural MMO
Code for the paper "Neural MMO: A Massively Multiagent Game..."
...Agents learn behaviors in a shared ecosystem that supports long-term training and emergent dynamics across large populations. The project is built to test scalability in multi-agent reinforcement learning, with features such as procedurally generated terrain and configurable game mechanics. While the original release has since been succeeded by newer versions maintained outside OpenAI, it remains a landmark framework for studying large-scale agent interactions in complex environments. -
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Coach
Enables easy experimentation with state of the art algorithms
Coach is a python framework that models the interaction between an agent and an environment in a modular way. With Coach, it is possible to model an agent by combining various building blocks, and training the agent on multiple environments. The available environments allow testing the agent in different fields such as robotics, autonomous driving, games and more. It exposes a set of easy-to-use APIs for experimenting with new RL algorithms and allows simple integration of new environments to solve. ... -
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Scalable Distributed Deep-RL
A TensorFlow implementation of Scalable Distributed Deep-RL
Scalable Agent is the open implementation of IMPALA (Importance Weighted Actor-Learner Architectures), a highly scalable distributed reinforcement learning framework developed by Google DeepMind. IMPALA introduced a new paradigm for efficiently training agents across large-scale environments by decoupling acting and learning processes. In this architecture, multiple actor processes interact with their environments in parallel to collect trajectories, which are then asynchronously sent to a centralized learner for policy updates. ... -
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Requests-HTML
Pythonic HTML Parsing for Humans
...When using this library you automatically get full JavaScript support! (Using Chromium, thanks to puppeteer) CSS Selectors (a.k.a jQuery-style, thanks to PyQuery). XPath Selectors, for the faint of heart. Mocked user-agent (like a real web browser). Automatic following of redirects. Connection–pooling and cookie persistence. The Requests experience you know and love, with magical parsing abilities, and async support. The rest of the code operates the same way as the synchronous version except that results is a list containing multiple response objects however the same basic processes can be applied as above to extract the data you want. -
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AESOP-ACP
A simulation framework for production process modeling in Python
ACP is a simulation framework aimed to production processes modeling. Compared to its ancestor - jES - it is lightweight, more general and written in Python. The basic goal of the simulator is to find bottlenecks in production process which could be hard to detect with traditional approaches (e.g., top-down). In addition, it can be used to speculate about “what-if” scenarios in order to suggest solution strategies. -
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Universe Starter Agent
A starter agent that can solve a number of universe environments
The universe-starter-agent repository is an archived OpenAI codebase designed as a starter reinforcement-learning agent that can interact with and solve tasks in OpenAI’s Universe environment platform. Its purpose is to serve as a baseline or reference implementation so researchers or developers can see how to build agents that operate in real-time, visual environments (e.g., games, browser apps) via pixel observations and keyboard/mouse actions. -
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EvalAI
Evaluating state of the art in AI
...If the challenge needs extra computational power, challenge organizers can easily add their own cluster of worker nodes to process participant submissions while we take care of hosting the challenge, handling user submissions, and maintaining the leaderboard. EvalAI lets participants submit code for their agent in the form of docker images which are evaluated against test environments on the evaluation server. During the evaluation, the worker fetches the image, test environment, and model snapshot and spins up a new container to perform the evaluation. -
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Madara
Middleware for distributed applications
The purpose of the project is to develop a portable programming framework that facilitates distributed and multi-threaded programming for C++, Java, and Python. MADARA was originally developed as an agent-based middleware specifically for real-time, distributed artificial intelligence, but is now more general purpose for distributed timing, control, knowledge and reasoning, and quality-of-service. MADARA is composed of several tools and middleware, and the main entry point into the system is the Knowledge and Reasoning Language (KaRL) Engine, which provides a real-time scripting language for nanosecond execution times hooked into a flexible transport layer for distributed reasoning. ... -
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survol
RDF-based framework monitoring business systems activity
A Python agent and a web interface aiming to help the analysis and investigation of a legacy application. A set of machines, processes, databases, programs etc ... all communicating with each other, manipulating your data, and whose software architecture has become, with time, complicated, difficult to understand, and undocumented. Data are aggregated with an RDF inference engine, creating a global vision of the business information processing. -
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PyGOAPng
Python Goal Oriented Action Planning (GOAP) library
A library for implementing GOAP in an AI agent. Based on pygoap v3 by Leif Theden et al. Updated code to work without having pygame installed, bug-fixed functions to make them implement the behaviors that were expected, and implemented desired behaviors so that the Pirate demo works properly for all known actions and goals. -
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Universe
Software for measuring and training an AI's general intelligence
...It does this by packaging the program into a Docker container, and presenting the AI with the same interface a human uses: sending keyboard and mouse events, and receiving screen pixels. Our initial release contains over 1,000 environments in which an AI agent can take actions and gather observations. -
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AgentX is the common name for the SNMP Agent Extensibility Protocol defined by RFC 2741. This project is a python interface which provides support for AgentX for python programs.
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SNMP v1/v2c/v3 engine and apps written in pure-Python. Project moved to GitHub: https://github.com/etingof/pysnmp
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aCompute
Aims to enable researcher to tap in to mobile computing capability
This is a software agent based computing program that will enable researchers and other users to tap in computing power of machine available by sharing work load on the fly with zero configuration on network & resources A self organizing agent program that will understand network and its resource. where as the only job left to researcher is to split up jobs in several chunks of programs either parallel or sequential jobs and go issue the job (A visual Modeler or Scripting support need to be yet designed) Software agents will automatically manage the rest or resource management, sharing , cloning of tasks etc. ... -
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Question de temps
A time travel gone wrong / Une mission temporelle qui tourne mal
Un Agent Voyageur, coincé en 2014 à cause de son régulateur défectueux, consigne dans ses comptes-rendus de mission ses efforts pour retourner chez lui, à son époque... ------------------ An Agent Traveller is stuck in 2014 due to his malfunctionning regulator. Follow in his mission reports his desesparate efforts to get back home at his time. -
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pulseagain
Installing softwares on Windows remote Machine with pulse-again
Pulse-Again is a graphical user interface written in Python and Tkinter to deploy softwares on a few computers with pulse2 secure agent installed on them. To use it, you need to create a pair of ssh key and you must allow the connexion on the remote computer by exporting your public key in the configuration of pulse2 (ssh) of the remote computer. To do this, you can insert manually your public key in the authorized_keys or use ssh-copy-id. Another point is that you must allow your machine to add automaticaly fingerprint of the remote computer in your known_hosts file Here to download : http://spacemax.googlecode.com/files/pulse-again.7z
