et al. A self-tuning actor-critic algorithm. Adv. Neural Inf. Process. Syst. 33, 20913–20924 (2020). Google Scholar Zheng, Z., Oh, J. & Satinder, S. On learning intrinsic rewards for policy gradient methods. Preprint at https://doi.org/10.48550/arXiv.1804.06459 (2018). Sanders, ...
346 6.33 Delta: Deep Learning Transfer Using Feature Map With Attention For Convolutional Networks 7, 6, 6 0.47 Accept (Poster) 347 6.33 Neural Speed Reading With Structural-jump-lstm 7, 5, 7 0.94 Accept (Poster) 348 6.33 Policy Generalization In Capacity-limited Reinforcement Learning 7, 7,...
Knowledge integration in distributed data mining has received widespread attention that aims to integrate inconsistent information locating on distributed sites. Traditional integration methods become ineffective since they are unable to generate global knowledge, support advanced integration strategy, or make pr...
把数据样例的关联性， 通过双向LSTM的记忆细胞，加上attention给模拟出来呢？
[26] proposed Multiple-Actor-Attention-Critic (MAAC) that involves an attention mechanism to select relevant information for each agent during training. In cooperative settings, a group of agents has to coordinate to maximize a shared team reward. Therefore, the global Q function 𝑄𝜋(𝒐,...