simple_rl is a minimalistic Python library designed to streamline reinforcement learning research and education. It provides a consistent API for defining environments and agents, with built-in support for common RL paradigms including Q-learning, Monte Carlo methods, and dynamic programming algorithms like value and policy iteration. The framework includes sample environments such as GridWorld, MountainCar, and Multi-Armed Bandits, facilitating hands-on experimentation. Users can extend base classes to implement custom environments or agents, while utility functions handle logging, performance tracking, and policy evaluation. simple_rl's lightweight architecture and clear codebase make it ideal for rapid prototyping, teaching RL fundamentals, and benchmarking new algorithms in a reproducible, easy-to-understand environment.

CArtAgO (Common ARTifact Infrastructure for AGents Open environments) is a lightweight, extensible framework for implementing environment infrastructures in multi-agent systems. It introduces the concept of artifacts: first-class entities representing environment resources with defined operations, observable properties, and event interfaces. Developers define artifact types in Java, register them in environment classes, and expose operations and events for agent consumption. Agents interact with artifacts using standard actions (e.g., createArtifact, observe), receive asynchronous notifications of state changes, and coordinate through shared resources. CArtAgO integrates easily with agent platforms such as Jason, JaCaMo, JADE, and Spring Agent, enabling hybrid system development. The framework provides built-in support for artifact documentation, dynamic loading, and runtime monitoring, facilitating rapid prototyping of complex agent-based applications.