MultiAgentModel provides a unified API to define custom environments and agent classes for multi-agent scenarios. Developers can specify observation and action spaces, reward structures, and communication channels. Built-in support for popular RL algorithms like PPO, DQN, and A2C allows training with minimal configuration. Real-time visualization tools help monitor agent interactions and performance metrics. The modular architecture ensures easy integration of new algorithms and custom modules. It also includes a flexible configuration system for hyperparameter tuning, logging utilities for experiment tracking, and compatibility with OpenAI Gym environments for seamless portability. Users can collaborate on shared environments and replay logged sessions for analysis.

FMAS (Flexible Multi-Agent System) is an open-source Python library for building, running, and visualizing multi-agent simulations. You can define agents with custom decision logic, configure an environment model, set up messaging channels for communication, and execute scalable simulation runs. FMAS provides hooks for monitoring agent state, debugging interactions, and exporting results. Its modular architecture supports plugins for visualization, metrics collection, and integration with external data sources, making it ideal for research, education, and real-world prototypes of autonomous systems.

jason-RL adds a reinforcement learning layer to the Jason multi-agent framework, allowing AgentSpeak BDI agents to learn action-selection policies via reward feedback. It implements Q-learning and SARSA algorithms, supports configuration of learning parameters (learning rate, discount factor, exploration strategy), and logs training metrics. By defining reward functions in agent plans and running simulations, developers can observe agents improve decision making over time, adapting to changing environments without manual policy coding.