Multi-Agents System from Scratch provides a comprehensive set of Python modules to build, customize, and evaluate multi-agent environments from the ground up. Users can define world models, create agent classes with unique sensory inputs and action capabilities, and establish flexible communication protocols for cooperation or competition. The framework supports dynamic task allocation, strategic planning modules, and real-time performance tracking. Its modular architecture allows easy integration of custom algorithms, reward functions, and learning mechanisms. With built-in visualization tools and logging utilities, developers can monitor agent interactions and diagnose behavior patterns. Designed for extensibility and clarity, the system caters to both researchers exploring distributed AI and educators teaching agent-based modeling.

Selective Reincarnation introduces a dynamic population-based training mechanism tailored for multi-agent reinforcement learning. Each agent’s performance is regularly evaluated against predefined thresholds. When an agent’s performance falls below its peers, its weights are reset to those of the current top performer, effectively reincarnating it with proven behaviors. This approach maintains diversity by only resetting underperformers, minimizing destructive resets while guiding exploration toward high-reward policies. By enabling targeted heredity of neural network parameters, the pipeline reduces variance and accelerates convergence across cooperative or competitive multi-agent environments. Compatible with any policy gradient-based MARL algorithm, the implementation integrates seamlessly into PyTorch-based workflows and includes configurable hyperparameters for evaluation frequency, selection criteria, and reset strategy tuning.