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| 1 | Colonies of learning automata显示文摘 | Katja Verbeeck Ann Nowe | 2002 | IEEE Transactions on Systems Man and Cybernetics-Part B2002，32，6： | 1 |
| 2 | Generalized learning automata for multi-agent reinforcement learning显示文摘 | Yann-Michael De Hauwere Peter Vrancx Ann Nowe | 2010 | AI Communications2010，23，： | 1 |
| 3 | Colonies of learning automata显示文摘 | Katja Verbeeck Ann Nowe | 2002 | IEEE Transactions on Systems Man and Cybernetics PartB2002，32，6： | 1 |
| 4 | Safe reinforcement learning for multi-energy management systems with known constraint functions显示文摘Reinforcement learning(RL)is a promising optimal control technique for multi-energy management systems.It does not require a model a priori-reducing the upfront and ongoing project-specific engineering effort and is capable of learning better representations of the underlying system dynamics.However,vanilla RL does not provide constraint satisfaction guarantees—resulting in various potentially unsafe interactions within its environment.In this paper,we present two novel online model-free safe RL methods,namely SafeFallback and GiveSafe,where the safety constraint formulation is decoupled from the RL formulation.These provide hard-constraint satisfaction guarantees both during training and deployment of the(near)optimal policy.This is without the need of solving a mathematical program,resulting in less computational power requirements and more flexible constraint function formulations.In a simulated multi-energy systems case study we have shown that both methods start with a significantly higher utility compared to a vanilla RL benchmark and Optlayer benchmark(94,6%and 82,8%compared to 35,5%and 77,8%)and that the proposed SafeFallback method even can outperform the vanilla RL benchmark(102,9%to 100%).We conclude that both methods are viably safety constraint handling techniques applicable beyond RL,as demonstrated with random policies while still providing hard-constraint guarantees. | Glenn Ceusters Luis Ramirez Camargo Rüdiger Franke Ann Nowé Maarten Messagie | 2023 | Energy and AI2023，12，2： | 0 |
| 5 | 无线传感器网络能量自调Q路由算法显示文摘无线传感器是一种非常微小、精密的,内嵌微处理器的设备,其特点在于当传感器在区域内布点后,这些节点不能再充电;所以在无线传感器网络设计中,能量的开销及网络负载平衡是首先应考虑的因素。提出了一种能量自调式Q路由算法(Q routing with energy and position awareness)。使用开放的模拟器OMNET++。比较了QREA协议与传统的路由协议—完全位置路由(geographical routing)的性能。通过实验得出了一个近优化的路由模型。 | 章翔 刘晓霞 Ann Nowe Kris Steenhaut | 2007 | 控制工程2007，14，B05： | 0 |
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