PyGame Learning Environment (PLE) is an open-source Python framework designed to simplify the development, testing, and benchmarking of reinforcement learning agents within custom game scenarios. It provides a collection of lightweight Pygame-based games with built-in support for agent observations, discrete and continuous action spaces, reward shaping, and environment rendering. PLE features an easy-to-use API compatible with OpenAI Gym wrappers, enabling seamless integration with popular RL libraries such as Stable Baselines and TensorForce. Researchers and developers can customize game parameters, implement new games, and leverage vectorized environments for accelerated training. With active community contributions and extensive documentation, PLE serves as a versatile platform for academic research, education, and real-world RL application prototyping.

AI-Agentic Machine Translation is an open-source framework designed for research and development in machine translation. It orchestrates three core agents—a generator, an evaluator, and a refiner—to collaboratively produce, assess, and refine translations. Built on PyTorch and transformer models, the system supports supervised pre-training, reinforcement learning optimization, and configurable agent policies. Users can benchmark on standard datasets, track BLEU scores, and extend the pipeline with custom agents or reward functions to explore agentic collaboration in translation tasks.

CityLearn provides a modular simulation platform for energy management research using reinforcement learning. Users can define multi-zone building clusters, configure HVAC systems, storage units, and renewable sources, then train RL agents against demand response events. The environment exposes state observations like temperatures, load profiles, and energy prices, while actions control setpoints and storage dispatch. A flexible reward API allows custom metrics—such as cost savings or emission reductions—and logging utilities support performance analysis. CityLearn is ideal for benchmarking, curriculum learning, and developing novel control strategies in a reproducible research framework.