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This paper compares rule-based and reinforcement learning pricing mechanisms for peer-to-peer electricity trading in residential photovoltaic communities, showing that rule-based methods are competitive and battery storage enhances RL performance.
The paper proposes SADQ, a modification to Q-learning that uses one-step rollout predictions from a dynamics model to regularize TD target aggregation, reducing bootstrap-induced overestimation and improving training stability across benchmarks.
This paper presents a deep Q-network-based multi-agent reinforcement learning framework for decentralized conflict resolution among heterogeneous small UAVs and eVTOL aircraft operating under degraded surveillance conditions, evaluating policies across 90 combinations of traffic density and separation thresholds.
QSplitFL proposes a DQN-based framework for optimal split point selection in split federated learning, using client hardware metrics to adapt to heterogeneous devices. Experiments show improved convergence and accuracy across multiple datasets and architectures.