Research Index

A novel framework combining LLM-based reasoning with reinforcement learning to optimize microgrid energy management while adapting to real-world uncertainties. The system uses natural language processing to interpret complex grid conditions and environmental factors, then employs multi-agent RL to coordinate distributed energy resources and storage systems.

A novel framework combining bio-inspired multi-agent systems with deep reinforcement learning for optimizing EV charging networks. The system uses MAS-GPT for agent coordination and implements nature-inspired optimization for both energy distribution and charging station placement, while considering real-time grid constraints and user behavior patterns.

A novel approach combining diffusion LLMs with reinforcement learning to optimize electric vehicle charging and microgrid energy management. The system uses parallel generation capabilities of diffusion models to simulate multiple energy scenarios simultaneously while adapting to real-time grid conditions through RL-based optimization.

A novel framework combining large language model agents with distributed control systems for autonomous microgrid management. The system uses LLM-powered agents to handle both high-level planning and low-level control decisions, while leveraging hybrid-triggered communication for efficient coordination across the network.

A novel approach combining test-time reinforcement learning (TTRL) with microgrid energy management to create self-optimizing power systems. The system continuously learns from operational data without requiring explicit labels, enabling real-time adaptation to changing conditions while maintaining grid stability and optimizing energy usage patterns.