Search Results for "actor"
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Showing 16 open source projects for "actor"
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MCP Server RAG Web Browser
A MCP Server for the RAG Web Browser Actor
The MCP Server for the RAG Web Browser Actor allows AI assistants and LLMs to perform web searches and extract information from web pages. It facilitates interaction with the web, enabling up-to-date context retrieval for AI applications. -
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RL with PyTorch
Clean, Robust, and Unified PyTorch implementation
...The project focuses on helping developers and researchers understand reinforcement learning methods by providing clean and reproducible implementations of well-known algorithms. It includes code for popular deep reinforcement learning techniques such as Deep Q-Networks, policy gradient methods, actor-critic architectures, and other modern RL approaches. The repository is structured so that users can easily experiment with different algorithms and training environments. Many examples demonstrate how agents learn to interact with simulated environments through trial and error using reinforcement learning principles. The codebase emphasizes clarity and modular design so that researchers can extend the implementations or use them for experimentation and benchmarking. -
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Swift Concurrency Agent Skill
Add expert Swift Concurrency guidance to your AI coding tool
...The skill codifies practical best practices for writing efficient, safe, and modern concurrent Swift code and outlines how to modernize existing legacy code toward Swift 6 conventions. Rather than teaching basic Swift, it targets the nuanced behaviors of concurrency primitives, actor isolation, and safety annotations like @MainActor and Sendable. It also clarifies how to reason about structured tasks, cancellation, and performance trade-offs. -
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RL Games
RL implementations
rl_games is a high-performance reinforcement learning framework optimized for GPU-based training, particularly in environments like robotics and continuous control tasks. It supports advanced algorithms and is built with PyTorch. -
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OAGI Python SDK
Python SDK for the Computer Use model Lux, developed by OpenAGI
OAGI Python SDK is a Python client library for the Lux computer-use model that turns Lux into a programmable automation layer for operating human-facing software via vision and actions. It exposes the OAGI API in an ergonomic way, letting you trigger Lux in three main modes: Tasker for precise scripted sequences, Actor for fast one-shot tasks, and Thinker for open-ended, multi-step objectives. The SDK is designed around “computer use” as a paradigm, where the AI actually navigates interfaces, clicks, types, scrolls, and reads the screen through screenshots instead of only calling APIs. It provides high-level asynchronous agents (like AsyncDefaultAgent and AsyncActor) that encapsulate the loop of capturing screenshots, sending them to Lux, interpreting responses, and executing UI actions with PyAutoGUI. ... -
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OpenShorts
Free & open source AI video platform
OpenShorts is an open-source, self-hosted AI video automation platform designed to generate, edit, and distribute short-form vertical content across social media platforms. It combines multiple tools into a single pipeline, including clip generation, AI-driven video creation, and YouTube optimization features. The system can transform long videos or uploaded files into short clips by detecting engaging moments, reframing content, and adding subtitles and visual effects. It also supports... -
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LangGraph
Build resilient language agents as graphs
LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. ... -
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minimalRL-pytorch
Implementations of basic RL algorithms with minimal lines of codes
...Each algorithm implementation is contained within a single file and typically ranges from about 100 to 150 lines of code, making it easy for learners to inspect the entire implementation at once. The repository includes examples of widely used reinforcement learning methods such as REINFORCE, Deep Q-Networks, Proximal Policy Optimization, and Actor-Critic architectures. Most experiments are designed to run quickly using the CartPole environment so that users can focus on understanding algorithm logic rather than computational infrastructure. -
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Multi-Agent Particle Envs
Code for a multi-agent particle environment used in a paper
Multiagent Particle Environments is a lightweight framework for simulating multi-agent reinforcement learning tasks in a continuous observation space with discrete action settings. It was originally developed by OpenAI and used in the influential paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The environment provides simple particle-based worlds with simulated physics, where agents can move, communicate, and interact with each other. Scenarios are designed to model cooperative, competitive, and mixed interactions among agents, making it useful for testing algorithms in multi-agent settings. ... -
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neurojs
A JavaScript deep learning and reinforcement learning library
...It focuses particularly on reinforcement learning algorithms, enabling developers to create intelligent agents that learn through interaction with simulated environments. The framework supports neural network architectures and reinforcement learning methods such as deep Q-networks and actor-critic algorithms. Several interactive demonstrations included with the project illustrate how neural networks can be used to train agents in simulated tasks, including a browser-based self-driving car example. These demos allow users to visualize how reinforcement learning agents improve their behavior over time as they receive rewards and update their neural networks. -
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Deep Reinforcement Learning for Keras
Deep Reinforcement Learning for Keras.
keras-rl implements some state-of-the-art deep reinforcement learning algorithms in Python and seamlessly integrates with the deep learning library Keras. Furthermore, keras-rl works with OpenAI Gym out of the box. This means that evaluating and playing around with different algorithms is easy. Of course, you can extend keras-rl according to your own needs. You can use built-in Keras callbacks and metrics or define your own. Even more so, it is easy to implement your own environments and... -
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Universe Starter Agent
A starter agent that can solve a number of universe environments
...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. Under the hood, this starter agent implements a version of the A3C (Asynchronous Advantage Actor-Critic) algorithm, adapted for the specific challenges of Universe environments (e.g., network latency, VNC streaming, asynchronous observations). The repo includes modules like train.py, worker.py, model.py, a3c.py, and envs.py to support training, parallel worker management, policy/critics, and environment wrappers. -
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april
Simulating worlds in a computer
The April project defines a set of C++ objects (World, Actor, Sensor, Reflex, Brain, Actuator, Event) that help simulate various environments. The world has an amount of energy that the user may use to create Actors and components while Actors may also create other Actors from their own energy. The code is structured in several layers: - april-core simply defines the library and the objects - april-gui builds on the concepts in april-core, giving a visual interpretation to the concepts - AprilDream is a GUI program that uses april-gui objects to display a World in evolution. ... -
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Cognitive agent based social simulation toolkit (RBOT+MRS) / production system based on ACT-R (http://act-r.psy.cmu.edu) allows for modelling single actor cognitive experiments (RBOT) and multiple actors in a simulated (semiotic) world (RBOT + MRS).
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Actor is a program you can have a conversation with (although perhaps not one which always makes a lot of sense). It analyzes what the user 'says' to it and generates replies based on learned vocabulary and word relationships.
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