deepmind/pysc2

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.

Star history