An LLM-enhanced MARL system with differential attention critic produces lower economic costs and voltage violations than baselines in simulated real-time P2P electricity trading.
Available: https://arxiv.org/abs/2412.18511
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SARAD is a hybrid LLM-DRL framework for autonomous driving that replaces random exploration with RAG-enhanced LLM guidance, an attention discriminator, and a collision predictor, reporting performance gains in the Highway-Env simulator.
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LLM-Enhanced Multi-Agent Reinforcement Learning with Expert Workflow for Real-Time P2P Energy Trading
An LLM-enhanced MARL system with differential attention critic produces lower economic costs and voltage violations than baselines in simulated real-time P2P electricity trading.
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SARAD: LLM-Based Safety-Aware Hybrid Reinforcement Learning with Collision Prediction for Autonomous Driving
SARAD is a hybrid LLM-DRL framework for autonomous driving that replaces random exploration with RAG-enhanced LLM guidance, an attention discriminator, and a collision predictor, reporting performance gains in the Highway-Env simulator.