AI-Based Simulation Environments

Abstract

AI-based simulation environments -- synthetic worlds where AI agents can be trained, tested, and evaluated -- have become essential infrastructure for reinforcement learning, robotics, autonomous vehicles, game AI, and agent evaluation. Simulations enable data collection at scales impossible in the real world, safe exploration of dangerous scenarios, systematic variation of environment conditions, and reproducible evaluation of AI policies. This study evaluates ten simulation environments across five application domains: robotics manipulation (Isaac Lab, MuJoCo 3.0, PyBullet-Ext), autonomous driving (CARLA 0.9.15, SUMO-AI, MetaDrive), game AI and multi-agent (OpenSpiel, PettingZoo-Ext), scientific discovery (ChemSim, BioSim-Gen), and general-purpose AI agent evaluation (GAIA-Sim, AgentBench-Env). Evaluation covers physical fidelity, rendering quality, simulation speed (steps per second), sim-to-real transfer quality, API usability, and AI training efficiency. A proposed Unified Simulation Quality Index (USQI) aggregates across all six dimensions. Isaac Lab achieves the highest USQI for robotics (8.42), CARLA for autonomous driving (8.24). MetaDrive achieves the best training efficiency (284,000 steps/second on CPU, 10x faster than CARLA). ChemSim achieves 94.2% agreement with DFT calculations for molecular property prediction. A simulation environment selection framework and best practices for sim-to-real transfer are proposed.