Decision transformer

Decision Transformer是一个将强化学习问题转化为条件序列建模的体系结构。它利用了Transformer体系结构的简单性和可伸缩性，以及语言建模方面的相关经验，如GPT-x和BERT。与传统的强化学习方法不同，Decision Transformer通过利用随机屏蔽Transformer输出来确定最优操作。通过将自回归模型设置在期望的回报、过去的状态和行动上，Decision Transformer模型可以生成实现期望回报的未来行动。尽管简单，Decision Transformer在Atari、OpenAI Gym和Key-to-Door任务上的性能与或超过了最先进的强化学习基线模型。[1]

Decision Transformer的网络结构可以看作是Transformer的Decoder部分的修改，主要使用了masked multi-head self-attention。它是一种纯监督学习方法，用于解决离线强化学习问题。与传统的马尔科夫决策过程(MDP)不同，Decision Transformer在训练时可以获取非常长期的信息，不再满足马尔科夫性。[2][3]

相关问题

Decision Transformer

Decision Transformer是一个将强化学习问题转化为条件序列建模的体系结构。它利用了Transformer体系结构的简单性和可伸缩性，以及语言建模方面的相关经验，如GPT-x和BERT。与传统的强化学习方法不同，Decision Transformer通过利用随机屏蔽Transformer输出来确定最优操作。通过将自回归模型设置在期望的回报、过去的状态和行动上，Decision Transformer可以生成实现期望回报的未来行动。尽管简单，Decision Transformer在Atari、OpenAI Gym和Key-to-Door任务上的性能与或超过了最先进的强化学习基线模型。\[1\]

Decision Transformer的网络结构可以看作是Transformer的Decoder部分的修改，主要使用了masked multi-head self-attention。\[2\]它是一种纯监督学习方法，用于解决离线强化学习问题。与传统的马尔科夫决策过程(MDP)不同，Decision Transformer在训练时可以获取非常长期的信息，不再满足马尔科夫性。\[3\]
#### 引用[.reference_title]
- *1* [决策Transformer:通过序列建模的强化学习](https://blog.csdn.net/zzh1464501547/article/details/117756403)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
- *2* *3* [NeurIPS论文解读｜Decision Transformer: 通过序列建模解决离线强化学习问题](https://blog.csdn.net/m0_55289267/article/details/125871135)[target="_blank" data-report-click={"spm":"1018.2226.3001.9630","extra":{"utm_source":"vip_chatgpt_common_search_pc_result","utm_medium":"distribute.pc_search_result.none-task-cask-2~all~insert_cask~default-1-null.142^v91^insertT0,239^v3^insert_chatgpt"}} ] [.reference_item]
[ .reference_list ]

decision transformer

Decision Transformer is a recent development in the field of machine learning and artificial intelligence. It is a type of transformer architecture that is designed to perform decision-making tasks, such as reinforcement learning and predictive modeling. Decision Transformer is unique in that it includes a decision module that takes into account the decision-making process in its calculations, which allows it to make more accurate and informed decisions. This technology is still in its early stages, but it shows great potential for improving the efficiency and effectiveness of machine learning models.