Training on broader task distributions for better generalization: 以前的few-shot方法一般假设是reward或dynamic的参数发生一点改变所形成的较窄的任务分布,但是这种任务分布下meta-training所学到的将难以泛化到较宽的任务分布上。某机器人的benchmark引入了不仅是修改
guiding algorithm design for improved model learning,model utilization,and policy training.In addition,we discuss the recent developments of model-based techniques in other forms of RL,such as offline RL,goal-conditioned RL,multi-agent RL,and meta-RL.Furthermore,we discuss the applicability and ...
这篇文章是21年5月发表在IEEE Transactions on Pattern Analysis and Machine Intelligence上的一篇文章,到现在引用量近700次,主要关注了元学习(Meta Learning)的定义,主要算法,以及在多个领域及场景上的应用。 然后这篇笔记并不是一篇综述的翻译,而是按照survey的架构进行的重要概念描述,结合自己的理解尽可能详细地把一...
[2] Sergey Levine, Aviral Kumar, George Tucker and Justin Fu. “Offline Reinforcement Learning: Tutorial, Review, and Perspectives on Open Problems”(2020). [3] CS 285 Deep Reinforcement Learninghttps://rail.eecs.berkeley.edu/deeprlcourse/ [4] CS330 Fall 2021 Deep Multi-Task and Meta Learn...
Very recently, deep reinforcement learning discovered a more efficient algorithm to perform matrix multiplication [22], while fine-tuning a pre-trained large language model on computer code allowed to solve university-level math questions at a human level [23]. These stunning achievements partially ...
Curriculum Learning Data Augmentation Deep Learning General Methods Deep Reinforcement Learning Diffusion Models Federated Learning Few-Shot and Zero-Shot Learning General Machine Learning Generative Adversarial Networks Graph Neural Networks Interpretability and Analysis Knowledge Distillation Meta Learning Metric Le...
In the previous case, at every step, a learning agent focuses on exactly one virtual node from the current VNR and generates a certain substrate node to host the virtual node. Link embedding is then performed separately in the same time step. To solve the VNE problem efficiently, the mapping...
Meta-learning算法使智能体能够从少量经验中快速适应新任务并学习新技能,并受益于他们对世界的先验知识。[127]的作者通过在一组相互关联的任务上训练循环神经网络来解决这个问题,其中网络输入包括除了在前一个时间步中收到的奖励之外选择的动作。因此,智能体被训练来学习动态地利用问题的结构并通过调整其隐藏状态来解决...
Most existing research on meta-learning can be divided into three categories: model-based, metric-based, and optimization-based, according to different usages [12]. In addition, some other meta-learning models have been proposed in recent years. According to some research results applied in the ...
A Survey on Model-based Reinforcement Learning 文章对MBRL做了总结,从概述开始,第一大部分是环境模型的学习,从tabular开始到neural network approximation,学习的方法是用一些prediction loss以及其他方法,去证明 error bound 更小。第二大部分,是环境模型的利用,最容易想到的就是直接用环境模型做planning,其次还有利用...