Overview:
This repository contains a collection of advanced simulation environments focused on multi-agent interactions, adaptive learning, and ecosystem dynamics. Each file represents a unique approach to agent behavior, environmental modeling, and AI integration. Designed for researchers, developers, and educators, this suite provides robust tools for experimenting with adaptive agents, Q-learning, neural networks, and complex ecosystems. From basic setups to sophisticated learning models, the simulations cater to a variety of use cases in artificial intelligence and behavioral simulations.
Key Highlights:
- Interactive Controls: Real-time adjustment of simulation parameters like agent speed, threat aggressiveness, and environmental conditions.
- Adaptive Learning Models: Integration of Q-learning and neural networks for dynamic agent behavior.
- Ecosystem Simulations: Realistic predator-prey dynamics and resource management.
- Advanced Visual Effects: Expanding maps, seasonal changes, and multi-layered environments.
- Offline and Online Support: Options for offline simulation as well as TensorFlow.js-based active learning.
Here is a detailed breakdown of each file included in this repository:
Description:
This file demonstrates a dynamic multi-agent simulation environment with user-controllable parameters. Features include adjustable agent speeds and threat aggressiveness, a live event log for simulation updates, and real-time performance metrics like FPS and step time. Designed as a foundational template for adaptive control in simulations.
Key Features:
- Adjustable agent speed and threat aggression via sliders.
- Real-time performance monitoring (FPS, simulation step time).
- Event log for tracking significant simulation events.
- Minimalistic design with responsive resizing.
Description:
A robust ecosystem simulator that tracks population metrics for plants, herbivores, and carnivores. Includes interactive controls to adjust population counts and a graphical representation of species distributions. This file is ideal for exploring predator-prey dynamics and simulating ecosystem changes over time.
Key Features:
- Population metrics visualization through bar graphs.
- Adjustable population sizes for plants, herbivores, and carnivores.
- Weather simulation with temperature and precipitation changes.
- Dynamic reproduction, growth, and predation behaviors.
Description:
A cutting-edge simulation file featuring agents equipped with neural networks for active learning. The agents use pre-training and on-the-fly data augmentation to adapt to environmental challenges. This simulation serves as a showcase for integrating machine learning models in real-time environments.
Key Features:
- Agents with pre-trained neural networks built using TensorFlow.js.
- Real-time learning through environmental interactions and data augmentation.
- Supports dynamic decision-making using a transformation and voting mechanism.
- Fully integrated event log for monitoring agent behavior.
Description:
This simulation introduces dual neural network architecture to optimize agent behavior in dynamic environments. Features include seasonal changes, circadian rhythm effects, and an interactive scoreboard. The system is designed for adaptive simulations with complex environmental variables.
Key Features:
- Dual neural network integration for agents.
- Seasonal simulation with day-night cycles and circadian adjustments.
- Global scoreboard tracking agent survival and task completion.
- Expandable environmental parameters for agent behavior testing.
Description:
A simulation environment designed for offline use, showcasing an expanding map radius and multi-layered weather and season systems. Includes political events and adaptive agents interacting within an increasingly larger space.
Key Features:
- Expanding map radius as the simulation progresses.
- Offline capabilities with preloaded resources and no external requests.
- Political events influencing agent behavior and resource availability.
- Advanced visual effects like illusions and map distortions.
Description:
This file features a Q-learning-driven simulation environment. Agents learn through reward-based actions, adapting to threats, food, and environmental changes. Incorporates advanced Q-learning parameters and a multi-faceted environment.
Key Features:
- Q-learning agents with state-action tables and reward systems.
- Simulation of tasks, obstacles, and shelters for agent interaction.
- Dynamic agent reproduction and genetic inheritance of traits.
- Day-night cycle influencing agent behavior.
Description:
A baseline simulation environment with core features such as agents, threats, food, and tasks. This file serves as an introductory template for building more complex multi-agent simulations.
Key Features:
- Basic agent and threat interaction mechanics.
- Adjustable parameters for agent speed and threat aggressiveness.
- Real-time performance metrics and an event log.
- Minimal setup for quick prototyping.
Description:
A comprehensive simulation combining Q-learning, population dynamics, and adaptive behavior in a unified environment. This file showcases the full spectrum of agent behaviors, environmental variables, and dynamic learning processes.
Key Features:
- Integration of Q-learning with real-time agent decision-making.
- Ecosystem-level simulation with plants, threats, food, and shelters.
- Multi-layered agent interactions with tasks and reproduction.
- Advanced logging and visualization for in-depth analysis.
Description:
An advanced simulation featuring multi-layered neural networks with quantum-inspired elements. Agents interact with threats and food sources while optimizing behavior through pre-trained models.
Key Features:
- Neural networks with quantum-inspired hidden layers.
- Pre-trained agent models for enhanced decision-making.
- Real-time energy management and environmental sensing.
- Dynamic interactions with food and threats influencing survival rates.