这个setting的优势在于:① 它保持了仅使用offline data完成适应的优势,但是相较于offline meta-train更加容易 ② 设计一个好的offline RL算法是困难的,而结合meta-RL能够进一步提升这种可能
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 ...
[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...
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...
We then propose a new taxonomy that provides a more comprehensive breakdown of the space of meta-learning methods today. We survey promising applications and successes of meta-learning including few-shot learning, reinforcement learning and architecture search. Finally, we discuss outstanding challenges ...
Here we need to have an overview of the DRL algorithm taxonomy. As we can see, DRL is a fast-developing field, and it is not easy to draw the taxonomy accurately. We can have some traditional perspectives for this, which ignore the most advanced area of DRL (such as meta-learning, tr...
Deep Reinforcement Learning Federated Learning Few-Shot and Zero-Shot Learning General Machine Learning Generative Adversarial Networks Graph Neural Networks Interpretability and Analysis Meta Learning Metric Learning ML Applications Model Compression and Acceleration Multi-Task and Multi-View Learning NLP inspire...
We have described the most promising approaches in machine learning, namely the natural gradient and mirror descent algorithms, which are well described in [24]. Table 1. Summary of review papers on optimization algorithms in the theory of neural networks. The main contribution of this review ...
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...
Multi-agent RL 目前不太感兴趣。。。 Meta-RL + model-based RL 很感兴趣但是了解的太少了,多看一看之后再来补。。。 最后放一个在ICML Tutorial on Model-Based Methods in Reinforcement Learning中的MBRL的分类。