This paper surveys the field of deep multiagent reinforcement learning (RL). The combination of deep neural networks with RL has gained increased traction
Tong H, Qian K, Halilaj E, Zhang YZ (2023) SRL-assisted AFM: Generating planar unstructured quadrilateral meshes with supervised and reinforcement learning-assisted advancing front method 72:102109 Google Scholar Manevitz LM, Yousef M, Givoli D (2002) Finite-element mesh generation using self-...
This is a list of documents which might help you while doing the project... 1. Open test1 excel file. 2. open the python file. 3. read the word document. 4. this should tell you how to run the project. - OLDCODE_ReinforcementlearningWithDestillationColum
This is a list of documents which might help you while doing the project... 1. Open test1 excel file. 2. open the python file. 3. read the word document. 4. this should tell you how to run the project. - OLDCODE_ReinforcementlearningWithDestillationColum
GI Buckley,JM Malouff - 《J Psychol》 被引量: 25发表: 2005年 Reinforcement control of observational learning in young children: A behavioral analysis of modeling This research was designed to analyze the role of direct reinforcement in vicarious reinforcement and observational learning, using individu...
Givan, and K. Driessens. Relational Reinforcement Learning: An Overview. In Proceedings of the Workshop on Relational Reinforcement Learning, Banff, Alberta, Canada, 2004.S. Dzeroski and L. de Raedt. Relational reinforcement learning. In Proceedings of the Fifteenth International Conference on ...
Reinforcement Learning and Optimal Control ASU, CSE 691, Winter 2020 Dimitri P. Bertsekas dimitrib@mit.edu Lecture 6 Bertsekas Reinforcement Learning 1 / 23 Outline 1 Parametric Approximation Architectures 2 Training of Approximation Architectures 3 Incremental Optimization of Sums of Differentiable ...
这就提出了一个问题,即应该探索更好的架构还是更好的(慢速)RL算法。为了确定瓶颈,我们使用监督学习来训练相同的策略结构,并使用Gittins指数方法生成的轨迹作为训练数据。我们发现,在测试域中执行后,学到的策略可以达到与Gittins指数方法相同的性能水平,这表明使用更好的RL算法仍有改进的余地。
Lagoudakis, M.G., Parr, R.: Learning in zero-sum team Markov games using factored value functions. In: Advances in Neural Information Processing Systems, pp. 1659–1666 (2003) Google Scholar Bernstein, D.S., Givan, R., Immerman, N., Zilberstein, S.: The complexity of decentralized ...
Influence maximization (IM) has been widely studied in recent decades, aiming to maximize the spread of influence over networks. Despite many works for sta