Seph's Biome 🌿🤖

Overview

Seph's Biome represents a pioneering venture in the realm of robotic operating systems, blending the intricacies of robotics, computer science, and evolutionary biology. The project focuses on the exploration of artificial evolution, with the objective of replicating and analyzing complex biological behaviors in a computational environment.

Getting Started 🚀

Prerequisites

• Python - latest release 🐍
• ROS Noetic (future updates to Foxy planned) 🤖
• Docker (optional) 🐳

Setup

To begin, clone the repository and navigate to the directory:

git clone 'https://github.com/LoQiseaking69/SephsBIOME.git'
cd SephsBIOME

Note: Full functionality will be available upon project completion. 🌟

Running the Project

Start the project using:

python3 main.py

An executable script (.sh) is planned for ease of use upon release. 😅

The initial modal

for the "GA" management and tuning (that is, this repository, The SephsBIOME Project, will slowly be converted into a shared rust library) the initial neural network is located at this repository; for isolating building and testing

Evolution of AI Integration 🌐🔧

Seph's Biome is now integrating a more sophisticated AI approach, utilizing a neural network model comprising an RBM Layer and a Q-Learning Layer. This model is designed to learn and adapt to complex environments, such as the BipedalWalker-v3 environment in Gym.

The new model architecture includes:

• RBM Layer: Responsible for feature extraction and representation learning from the input data.
• QLearning Layer: Utilized for reinforcement learning and decision-making, optimizing actions based on learned rewards and penalties.

This integration marks a significant advancement in Seph's Biome, enhancing its ability to learn and adapt to dynamic environments, leading to more effective and robust robotic behaviors.

Capabilities and Goals 🎯

Seph's Biome integrates concepts from robotics, AI, and biology, aiming to achieve the following objectives:

• Robotic Simulation: Employing ROS to simulate realistic robotic behaviors across different scenarios. This includes navigating environments, interacting with objects, and performing tasks autonomously. 🤖

• Evolutionary Algorithms for Real-Time Optimization: Implementing genetic algorithms to evolve and optimize complex robotic behaviors. This process is now enhanced by integrating a custom Q-learning layer, allowing the system to adapt and improve its performance over time. 🧬

• Neural Network Integration: Integrating a custom Q-learning layer into the system to facilitate decision-making and adaptive learning. The Q-learning model is fine-tuned and optimized using a genetic algorithm component designed for dynamic, runtime adjustments. This integration allows for efficient learning and adaptation in robotic behaviors. 🧠

• Real-Time Data Handling: Managing sensor data essential for real-time decisions and performance analysis. This includes processing inputs from various sensors such as cameras, LiDAR, and accelerometers to make informed decisions and adjust behavior accordingly. ⏱️

• Dynamic Visualization: Providing real-time visualization of key metrics to aid in understanding and analyzing the evolutionary and learning processes. This enables researchers and developers to monitor the system's performance, identify areas for improvement, and optimize its behavior in real-time. 📊

The overarching goal is to create a self-evolving robotic system that emulates and enhances biological processes and behaviors. Through iterative refinement of its genetic algorithms and reinforcement learning models, the system aims to achieve extensive multimodal hyperparameter tuning, leading to an optimized system capable of multimodal functionality in various robotics contexts. 🌱🤖