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estrutura de simulação

  • AgentSimulation is a Python framework for real-time 2D autonomous agent simulation with customizable steering behaviors.
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    What is AgentSimulation?
    AgentSimulation is an open-source Python library built on Pygame for simulating multiple autonomous agents in a 2D environment. It allows users to configure agent properties, steering behaviors (seek, flee, wander), collision detection, pathfinding, and interactive rules. With real-time rendering and modular design, it supports rapid prototyping, teaching simulations, and small-scale research in swarm intelligence or multi-agent interactions.
    AgentSimulation Core Features
    • Multiple steering behaviors (seek, flee, arrive, wander)
    • Customizable agent attributes (speed, acceleration)
    • Obstacle and collision avoidance
    • Real-time 2D visualization with Pygame
    • Modular environment and agent interaction rules
  • A Unity ML-Agents based environment for training cooperative multi-agent inspection tasks in customizable 3D virtual scenarios.
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    What is Multi-Agent Inspection Simulation?
    Multi-Agent Inspection Simulation provides a comprehensive framework for simulating and training multiple autonomous agents to perform inspection tasks cooperatively within Unity 3D environments. It integrates with the Unity ML-Agents toolkit, offering configurable scenes with inspection targets, adjustable reward functions, and agent behavior parameters. Researchers can script custom environments, define the number of agents, and set training curricula via Python APIs. The package supports parallel training sessions, TensorBoard logging, and customizable observations including raycasts, camera feeds, and positional data. By adjusting hyperparameters and environment complexity, users can benchmark reinforcement learning algorithms on coverage, efficiency, and coordination metrics. The open-source codebase encourages extension for robotics prototyping, cooperative AI research, and educational demonstrations in multi-agent systems.
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