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...
1. Insight 传统方法可以用RL, IL根据感知数据和语言来训练策略,用策略来输出动作。 而Voxposer的insight是把很多的任务拆解成一个实体entity在一个空间里一系列的空间变换,这个实体可以是机器人,物体,或者物体的某部分。 为了得到这一系列的空间变换,一个方法是可以让一个模型直接输出这一系列的空间变换。另外一个...
同样,终身学习者应该能有效扩展到大量的任务上去,同时从最小的数据中快速学习每个任务。 有效终身学习算法(ELLA)和PG-ELLA是分别针对在终身学习设定中分类/衰减任务和RL任务设计的。 对于每个任务模型,两种方法都假设了可以用共享知识库L进行因式分解的参数,从而促进任务之间的传递。具体来说,任务Z (t)的模型参数由...
以CLIP为代表的视觉语言大模型(VLMs)在zero-shot识别等领域表现出了优异的性能,这改变了很多下游任务的学习范式,研究者们纷纷尝试如何将VLMs集成到现有的框架中来提高下游性能。虽然CLIP在ImageNet等代表性数据集达到了较高的准确率,但是其不可避免的出现了长尾数据识别较差的现象。例如对于“night snake”等十多个...
本文提出了一个名为 VLM-RM 的方法,这是一种使用预训练的 VLMs 作为视觉基础 RL 任务的奖励模型的通用方法。具体来说,本文使用 CLIP 作为 VLM,将当前环境状态的 CLIP 嵌入和简单语言提示的 cos-相似度作为奖励函数。本文还可以通过提供描述环境中性状态的“基线提示”,并在计算奖励时将表示部分投影到基线提示和...
many domain shifts - even with no access to the target domain. DARLA significantly outperforms conventional baselines in zero-shot domain adaptation scenar- ios, an effect that holds across a variety of RL en- vironments (Jaco arm, DeepMind Lab) and base RL algorithms (DQN, A3C and EC)...
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 ...
Generalizing vision-based reinforcement learning (RL) agents to novel environments remains a difficult and open challenge. Current trends are to collect large-scale datasets or use data augmentation techniques to prevent overfitting and improve downstream generalization. However, the computational and data ...
We found that the peak signal-to-noise ratio (PSNR) and resolution of ZS-DeconvNet images were substantially better than those generated by analytical algorithms, such as the classic Richardson-Lucy (RL) and the latest developed sparse deconvolution5 (Fig. 1c–e) and the throughput rate of ...
我们通过引入DARLA(DisentAngled Repsentsentation Learning Agent)来证明如何通过DisentAngled Representation (解析表示)来提高RL算法的鲁棒性 DARLA依靠学习源域和目标域之间共享的潜在状态表示,通过学习环境生成因子的解开表示。至关重要的是,DARLA并不要求目标域数据形成其表示。我们的方法采用三阶段管道:1)学习看,2)学习...