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...
Voxposer:zero-shot获取实体的空间变换 作者是李飞飞高徒黄文龙 主页:VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models 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...
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。 对于新任务Z(tnew)给出唯一的描述符m(tnew),我们可以在学习字典D中潜在的描述符空间路径 LASSO上评估任务的嵌入: 由于S(t...
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 ...
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...
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...
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)...
. 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...
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 genera...
In this work, we take inspiration from recent advances in computational neuroscience and propose a model, Associative Latent DisentAnglement (ALDA), that builds on standard off-policy RL towards zero-shot generalization. Specifically, we revisit the role of latent disentanglement in RL and show how ...