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Ressourcensammlung

  • An agent-based simulation framework for demand response coordination in Virtual Power Plants using JADE.
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    What is JADE-DR-VPP?
    JADE-DR-VPP is an open-source Java framework that implements a multi-agent system for Virtual Power Plant (VPP) demand response (DR). Each agent represents a flexible load or generation unit that communicates via JADE messaging. The system orchestrates DR events, schedules load adjustments, and aggregates resources to meet grid signals. Users can configure agent behaviors, run large-scale simulations, and analyze performance metrics for energy management strategies.
    JADE-DR-VPP Core Features
    • Multi-agent demand response orchestration
    • Real-time JADE messaging between agents
    • Configurable load and generation agent models
    • VPP resource aggregation and dispatch
    • Simulation result export (CSV/JSON)
  • An RL environment simulating multiple cooperative and competitive agent miners collecting resources in a grid-based world for multi-agent learning.
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    What is Multi-Agent Miners?
    Multi-Agent Miners offers a grid-world environment where multiple autonomous miner agents navigate, dig, and collect resources while interacting with each other. It supports configurable map sizes, agent counts, and reward structures, allowing users to create competitive or cooperative scenarios. The framework integrates with popular RL libraries via PettingZoo, providing standardized APIs for reset, step, and render functions. Visualization modes and logging support help analyze behaviors and outcomes, making it ideal for research, education, and algorithm benchmarking in multi-agent reinforcement learning.
  • Open-source PyTorch-based framework implementing CommNet architecture for multi-agent reinforcement learning with inter-agent communication enabling collaborative decision-making.
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    What is CommNet?
    CommNet is a research-oriented library that implements the CommNet architecture, allowing multiple agents to share hidden states at each timestep and learn to coordinate actions in cooperative environments. It includes PyTorch model definitions, training and evaluation scripts, environment wrappers for OpenAI Gym, and utilities for customizing communication channels, agent counts, and network depths. Researchers and developers can use CommNet to prototype and benchmark inter-agent communication strategies on navigation, pursuit–evasion, and resource-collection tasks.
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