本文提出了 Decision Transformer,其输入为 desired return, past states 以及 past actions,输出为当前时间步的 action。这样做的好处是跳过了传统 TD Learning 中导致训练不稳定的因素。 本文考察 offline learning 这个设定,即我们的训练集来自于一个未知策略与环境交互的历史轨迹,而我们希望通过该数据集得到一个最...
Illustrative example of finding shortest path for a fixed graph (left) posed as reinforcement learning. Training dataset consists of random walk trajectories and their per-node returns-to-go (middle). Conditioned on a starting state and generating largest possible return at each node, Decision Tran...
在前面的部分中,我们确定了Decision Transformer可以产生有效的策略(actor)。我们现在评估Transformer模型是否也可以成为有效的critic。我们将Decision Transformer修改为在Key-to-Door环境中输出除了动作token之外的回报token。此外,没有给出第一个回报token,而是对其进行预测(即模型学习初始分布 ),类似于标准的自回归生成模型。
Transformer 作为近年非常火的模型,有很多人想要把它融合到 Offline RL 领域。过去的一些方法把将 Transformer 融入到传统 RL 的网络结构中,本文则是想要验证是否能单纯使用 Transformer 模型来解决 Offline RL 问题,也就是说要完全丢掉基于 Bellman 等式的 TD Learning 那一套,只把 Offline RL 作为序列预测问题来解...
Reinforcement learning(RL)has become a dominant decision-making paradigm and has achieved notable success in many real-world applications.Notably,deep neural networks play a crucial role in unlocking RL's potential in large-scale decision-making tasks.Inspired by current major success of Transformer ...
We introduce a framework that abstracts Reinforcement Learning (RL) as a sequence modeling problem. This allows us to draw upon the simplicity and scalability of the Transformer architecture, and associated advances in language modeling such as GPT-x and BERT. In particular, we present Decision Tra...
Official codebase for Decision Transformer: Reinforcement Learning via Sequence Modeling. - myelinio/decision-transformer
And today we are happy to announce that we integrated the Decision Transformer, an Offline Reinforcement Learning method, into the 🤗 transformers library and the Hugging Face Hub. We have some exciting plans for improving accessibility in the field of Deep RL and we are looking for...
DODT: Enhanced Online Decision Transformer Learning through Dreamer's Actor-Critic Trajectory Forecasting Advancements in reinforcement learning have led to the development of sophisticated models capable of learning complex decision-making tasks. However, effi... EH Jiang,Z Zhang,D Zhang,... 被引量:...
Decision Transformer: Reinforcement Learning via Sequence Modelingarxiv.org/abs/2106.01345 这是transformer在强化学习领域非常重要的工作,作者想借助Transformer架构的简洁性和可扩展性,对强化学习的state,action,reward序列结合因果掩码建模,把寻找最优action变成生成行为,而不是基于奖励函数、策略梯度。 transformer通过...