This repository provides an end-to-end reinforcement learning framework for StarCraft II gameplay research. The core agent uses Proximal Policy Optimization (PPO) to learn policy networks that interpret observation data from the PySC2 environment and output precise in-game actions. Developers can configure neural network layers, reward shaping, and training schedules to optimize performance. The system supports multiprocessing for efficient sample collection, logging utilities for monitoring training curves, and evaluation scripts for running trained policies against scripted or built-in AI opponents. The codebase is written in Python and leverages TensorFlow for model definition and optimization. Users can extend components such as custom reward functions, state preprocessing, or network architectures to suit specific research objectives.

Aporia's platform offers comprehensive monitoring and observability solutions for machine learning models. By enabling real-time tracking and analysis, the platform helps data science and ML engineering teams to quickly identify issues, optimize performance, and ensure the reliability and accuracy of AI models. Core functionalities include real-time guardrails, anomaly detection, and embedding drift monitoring, all of which are crucial for maintaining robust and secure AI systems.

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Mastra provides a visual interface for designing, testing, and deploying AI agents without writing code. Users can drag and drop nodes to create multi-step workflows that integrate language models, computer vision, and custom scripts. Data connectors allow seamless retrieval from databases, CRMs, or web services, while built-in analytics track agent performance. Teams can collaborate in real time, version control workflows, and roll back changes. Once tested, agents can be deployed with a single click to web chat widgets, messaging apps, or REST APIs. Monitoring dashboards surface usage metrics, error alerts, and optimization suggestions to maintain and improve agent effectiveness over time.