Offline RL is also a problem of generalisation: a key issue here is generalising to state-action pairs unseen in the training data, and most current methods tackle this by conservatively avoiding such pairs (Kumar et al., 2020).离线RL泛化的关键是对训练数据中未看到的状态动作对的泛化,当前的方...
如果两年前做这个zero-rl,大概率是不work,因为那个时候的base-model+sft都做的很烂,在这些base上跑zero-rl,自然不会有特别好的结果。经过2年多的发展,预训练模型已经比之前好了太多,包括预训练阶段加了更多的推理数据,退火阶段引入更多的高质量推理数据等等,让base-model在zero-shot上的效果已经可以比肩上一代的...
结合任务描述符,仅给出描述符,以确保我们的方法快速预测针对新任务的策略。进行zero-shot转移的操作是通过使用耦合字典学习来确保的,它允许我们在一个特征空间(例如任务描述符)观察数据实例,并利用字典和稀疏编码,在其它的特征空间中(例如策略参数)恢复其潜在的信号。
research@deepseek.com 摘要 我们介绍了我们第一代推理模型:DeepSeek-R1-Zero 和 DeepSeek-R1。DeepSeek-R1-Zero 是一个通过大规模强化学习(RL)训练的模型,在没有监督微调(SFT)作为初步步骤的情况下,展现出了显著的推理能力。通过强化学习,DeepSeek-R1-Zero 自然地形成
Zero-shot-RIS 本文提出了一种zero-shot的Referring image segmentation方法,该方法利用了来自CLIP的pre-train的跨模态知识。所提方法的性能明显优于所有基线方法和监督较弱的方法,向CLIP学习预训练跨模态!简单高效的零样本参考图像分割方法 原文链接:https://arxiv.org/abs/2303.17811v1...
Fig. 1: Zero-shot deconvolution networks. a The dual-stage architecture of ZS-DeconvNet and the schematic of its training phase. b The schematic of the inference phase of ZS-DeconvNet. c Representative SR images of Lyso and MTs reconstructed by RL deconvolution (second column), sparse deconvo...
. DARLA first learns a visual system that encodes the observations it receives from the environment as disentangled representations, in a completely unsupervised manner. It then uses these representations to learn a robust source policy that is capable of zero-shot domain adaptation...
In this work, we show that we can achieve a zero-shot language-to-behavior policy by first grounding the imagined sequences in real observations of an unsupervised RL agent and using a closed-form solution to i
As a step towards developing zero-shot task generalization capabilities in reinforcement learning (RL), we introduce a new RL problem where the agent should learn to execute sequences of instructions after learning useful skills that solve subtasks. In this problem, we consider two types of ...
Paper tables with annotated results for RL Zero: Zero-Shot Language to Behaviors without any Supervision