Contrastive Learning Of Structured World Models

时间 2021-01-02

标签图神经网络强化学习深度学习繁體版

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Paper : Contrastive Learning Of Structured World Models Code : official 摘要作者在强化学习任务中，提出了使用GNN来建模多物体之间的关系的方法，使用transE的思路来进行状态的预测，并通过Contrastive Loss 来进行训练，提出 C-SWM 模型。作者在雅达利游戏，多物体物理模拟等任务上达到了SOTA。方法 C

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1. A Simple Framework for Contrastive Learning of Visual Representations
2. Discriminative Embeddings of Latent Variable Models for Structured Data
3. （转）Applications of Reinforcement Learning in Real World
4. 《Learning Deep Structured Semantic Models for Web Search using Clickthrough Data 》论文总结
5. Visualizing Dataflow Graphs of Deep Learning Models in TensorFlow
6. Structured Learning-Linear Model
7. 论文5分钟 | supervised contrastive learning
8. [AAAI2018]Learning Structured Representation Representation for Text Classification via Reinforcemen
9. Improved Baselines with Momentum Contrastive Learning
10. Representation Learning with Contrastive Predictive Coding

>>更多相关文章<<