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.

MADDPG-Keras delivers a complete framework for multi-agent reinforcement learning research by implementing the MADDPG algorithm in Keras. It supports continuous action spaces, multiple agents, and standard OpenAI Gym environments. Researchers and developers can configure neural network architectures, training hyperparameters, and reward functions, then launch experiments with built-in logging and model checkpointing to accelerate multi-agent policy learning and benchmarking.