deepmind/pysc2
Pysc2
pysc2 هي واجهة Python وإطار عمل محاكاة يربط محرك لعبة StarCraft II بوكلاء التعلم الآلي. تعمل كغلاف API يكشف عن محتويات اللعبة كمجموعة من الملاحظات والإجراءات، مما يوفر بيئة تعلم تعزيزي للبحث والتدريب.
يتضمن إطار العمل أدوات لتحليل إعادة تشغيل اللعبة لاستخراج البيانات وتسلسلات الإجراءات من المباريات المسجلة للنمذجة التنبؤية. كما يوفر بيئة محاكاة للوكلاء لتشغيل وتقييم أداء وكلاء الذكاء الاصطناعي الفرديين أو المتنافسين.
يتعامل النظام مع إعداد خريطة اللعبة، والتحليل السلوكي، ويوفر واجهة تحكم يدوية لتصحيح سلوك الوكيل. يحول حالة اللعبة إلى موترات متعددة الأبعاد ويستخدم إطار عمل استدعاء إجراء عن بُعد لإدارة الاتصال بين العميل ومحرك اللعبة.
Features
- Reinforcement Learning Environments - Provides a specialized reinforcement learning environment connecting the StarCraft II engine to machine learning agents.
- Agent Simulation Environments - Offers a system for running and testing competing AI agents to evaluate their behavior and strategic performance.
- Autonomous Agent Simulations - Implements a framework for simulating and analyzing the behavior of autonomous AI agents in a strategic environment.
- Action-Observation Cycles - Implements a synchronous loop where agents receive game state snapshots and return corresponding action commands.
- Game Automation APIs - Provides a programmatic interface for autonomous agents to receive game observations and execute actions within the engine.
- Game Simulation Environments - Offers a simulated environment for running controlled game scenarios to evaluate AI controller performance.
- Game AI Development Kits - Provides a comprehensive development kit for creating autonomous agents that operate in a real-time strategy setting.
- Model Behavioral Analysis - Facilitates the study of agent decision-making and behavioral patterns using data extracted from recorded matches.
- Replay Processing Pipelines - Parses recorded game files into sequences of observations and actions to facilitate offline supervised learning.
- Tensor Data Representations - Transforms raw game state into multi-dimensional tensors for direct consumption by deep neural networks.
- Protobuf Serialization - Uses Protocol Buffers to serialize observations and actions for consistent, strictly typed cross-language data transfer.
- Game Replay Analysis - Ships a utility for extracting observations and actions from recorded game files to review agent behavior or train models.
- Game Replay Parsers - Provides a utility for extracting data and sequences of actions from recorded game files to train predictive models.
- gRPC Client Implementations - Implements a gRPC client to manage synchronous communication of game states and actions between Python and the engine.
- Process Isolation Architectures - Runs the game engine in a separate process to ensure that engine crashes do not interrupt the agent training process.
- Reinforcement Learning - StarCraft II learning environment.
- Reinforcement Learning Environments - Environment for reinforcement learning research in StarCraft II.
- Strategy Games - Official environment for StarCraft II reinforcement learning research.
