🚀 Learn how to Apply DQN Reinforcement Learning to Agents - AutoCodeAgent v1.8.0 - with Meta RL Rag

In this video, we're excited to unveil AutoCodeAgent v1.8.0!

🐙 🐙 🐙 GitHub Repository: https://github.com/samugit83/AutoCodeAgent2.0

🔥✨ What's New in Version 1.8.0 🔥✨

🚀 Dual-Mode Reinforcement Learning Agent:

Introduced a powerful and versatile Q-Learning agent that learns optimal policies directly from experience.

Offers two distinct operational modes:

🧮 Simple Mode (Tabular Q-Learning): Ideal for small, discrete problems, this mode stores state-action values in a traditional Q-table.

🧠 Neural Mode (Deep Q-Network - DQN): Harnesses a neural network to approximate Q-values, enabling advanced learning in complex, continuous state spaces.

Seamlessly integrates with Redis and WebSockets, allowing for potential real-time human feedback to enhance policy refinement, especially beneficial for the DQN approach.

🎯 RL-Powered Dynamic RAG Selection (RL Meta RAG):

Launched the innovative RL Meta RAG system that dynamically selects the optimal Retrieval-Augmented Generation (RAG) technique—including LlamaIndex, HyDE, and Adaptive RAG—based on query-specific characteristics.

Utilizes an advanced language model (LLM) to analyze and extract query features, such as complexity, domain, and ambiguity, guiding the RL agent's decision-making process.

Features a robust mechanism to alternate intelligently between RL-driven decisions and direct LLM suggestions, ensuring optimal performance and accuracy.


More About AutoCodeAgent 🚀

AutoCodeAgent redefines AI-powered problem solving by seamlessly integrating three groundbreaking modes:

IntelliChain 🔗:
Breaks down complex tasks with surgical precision using dynamic task decomposition and on-demand code generation. Each subtask is meticulously planned and executed for targeted efficiency.

Deep Search 🌐:
Harnesses the power of autonomous, real-time web research to extract the most current and comprehensive information. It transforms raw data from diverse online sources into actionable intelligence.

Multi-RAG 🤖:
Enhances information retrieval with an innovative multi-RAG framework that supports various RAG techniques. This approach delivers contextually rich, accurate, and coherent results across complex document types and knowledge structures. Plus, these RAG techniques are implemented as tools for versatile use—ideal for both practical applications and educational exploration via the provided .ipynb files in the /tools/rag folder.

AutoCodeAgent Key Features 🚀🔧

Task Decomposition 🧩:
Breaks down complex tasks into smaller subtasks for structured execution.
Dynamic Code Generation & Execution ⚙️:
Automatically generates Python code tailored to each subtask and executes it sequentially.
Flexible Tool Integration 🔌:
Easily integrate tools via Python libraries, custom functions, or pre-built modules.
Iterative Evaluation Loop 🔄:
Monitors execution, re-plans, and regenerates subtasks to ensure complete success.
Memory Logging & Error Handling 📝:
Captures detailed logs and gracefully manages errors for easier debugging.
Modular & Extensible Design 🔗:
Encourages reusability and expansion with minimal core changes.
Safe & Secure Execution 🔒:
Uses controlled namespaces and Python AST validation for secure, error-free code execution.
Python Function Validation & Task Regeneration ✅:
Inspects generated code for syntax issues, dangerous operations, and proper parameter usage before running.
RAG Capabilities 📚:
Integrates multiple RAG techniques for efficient data ingestion and retrieval using vector and graph databases.
Default Tools🛠️📚
The default tools are pre-implemented and fully functional, supporting the agent in executing subtasks. These default tools are listed below and can be found in the file: /code_agent/default_tools.py

browser_navigation
integration of SurfAi for web navigation, data and image extraction, with multimodal text + vision capabilities

helper_model
An LLM useful for processing the output of a subtask

search_web
A tool for searching information on the web

send_email
A tool for sending an email

and many other tools for RAG....

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Visit https://www.devergolabs.com for more exciting projects!