.github/workflows q_transformer .gitignore LICENSE README.md q-transformer.png setup.py Repository files navigation README MIT license Q-transformer Implementation of Q-Transformer, Scalable Offline Reinforcement Learning via Autoregressive Q-Functions, out of Google Deepmind I will be keeping...
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Q-Transformer: Scalable Offline Reinforcement Learning via Autoregressive Q-Functions 【CORL2023】来源:别人推荐作者:Googlle DeepMind / Servey Levinearxiv: https://arxiv.org/abs/2309.10150项目主页:qtransformer.github.io0 摘要适用对象:离线数据集和演示数据支持的多任务策略场景 使用Transformer作为Q-...
Github开源项目: https://q-transformer.github.io/q-transformer.github.io/ Transformer + offline 强化学习,DeepMind 让大模型成为机器人感知世界的大脑。 Abstract: 我们的方法通过使用Transformer通过离线时间差备份训练的为Q函数提供可扩展的表示。我们称我们的方法为Q-Transformer。通过离散每个动作维度并将每个...
作者通过实验证明,这些改进可以用于在使用高端GPU(NVIDIA A100)时实现与FP16相比的端到端推理加速,速度提高约3.25倍,而在使用更具成本效益的GPU(NVIDIA A6000)时速度提高了4.5倍。 代码:https://github.com/IST-DASLab/gptq 1、简介 来自Transformer家族的预训练生成模型,通常被称为GPT或OPT,已经在复杂语言建模任...
官方版TensorFlow实现:https://github.com/tensorflow/tensor2tensor 哈佛大学NLP组Pytorch实现:http://nlp.seas.harvard.edu/2018/04/03/attention.html 在本文中,我们会逐个概念进行介绍,希望能帮助没接触过Transformer的人能够更容易的理解。 从高层面看
The source code is available at https://github.com/zjukongming/TranSQ .Kong, MingZhejiang UniversityHuang, ZhengxingZhejiang UniversityKuang, KunKey Laboratory for Corneal Diseases Research of Zhejiang ProvinceZhu, QiangZhejiang UniversityWu, Fei
https://github.com/abaisero/gym-gridverse Fortunato M, Azar MG, Piot B, Menick J, Osband I, Graves A, Mnih V, Munos R, Hassabis D, Pietquin O, Blundell C, Legg S (2017) Noisy networks for exploration. arXiv:1706.10295. https://api.semanticscholar.org/CorpusID:5176587 Kurniawati ...
To our best knowledge, this is the first time that directly training SNNs have exceeded 85% accuracy on ImageNet-1K. The code and models are publicly available at https://github.com/zhouchenlin2096/QKFormer PDF Paper record Methods VSA vaswani2017attention SSA zhou2023spikformer SDSA yao2023...
论文:q-transformer.github.io 项目:q-transformer.github.io 在SDQN中,我们介绍了如何通过将多维动作分解为一维动作序列避免维度灾难的问题,Q-transformer中进一步扩展了该方法,利用transformer处理序列化之后的动作。在Q-transformer中,首先将多维连续动作进行逐维度的离散化处理,将每个维度的动作看做是RL中的一个时间步...