Towards continual reinforcement learning: A review and perspectives
In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …
Adventures in data analysis: A systematic review of Deep Learning techniques for pattern recognition in cyber-physical-social systems
Abstract Machine Learning (ML) and Deep Learning (DL) have achieved high success in
many textual, auditory, medical imaging, and visual recognition patterns. Concerning the …
many textual, auditory, medical imaging, and visual recognition patterns. Concerning the …
A survey of zero-shot generalisation in deep reinforcement learning
The study of zero-shot generalisation (ZSG) in deep Reinforcement Learning (RL) aims to
produce RL algorithms whose policies generalise well to novel unseen situations at …
produce RL algorithms whose policies generalise well to novel unseen situations at …
The statistical complexity of interactive decision making
A fundamental challenge in interactive learning and decision making, ranging from bandit
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …
problems to reinforcement learning, is to provide sample-efficient, adaptive learning …
Dive into deep learning
This open-source book represents our attempt to make deep learning approachable,
teaching readers the concepts, the context, and the code. The entire book is drafted in …
teaching readers the concepts, the context, and the code. The entire book is drafted in …
Bilinear classes: A structural framework for provable generalization in rl
Abstract This work introduces Bilinear Classes, a new structural framework, which permit
generalization in reinforcement learning in a wide variety of settings through the use of …
generalization in reinforcement learning in a wide variety of settings through the use of …
Learning invariant representations for reinforcement learning without reconstruction
We study how representation learning can accelerate reinforcement learning from rich
observations, such as images, without relying either on domain knowledge or pixel …
observations, such as images, without relying either on domain knowledge or pixel …
When is partially observable reinforcement learning not scary?
Partial observability is ubiquitous in applications of Reinforcement Learning (RL), in which
agents learn to make a sequence of decisions despite lacking complete information about …
agents learn to make a sequence of decisions despite lacking complete information about …
Representation learning for online and offline rl in low-rank mdps
This work studies the question of Representation Learning in RL: how can we learn a
compact low-dimensional representation such that on top of the representation we can …
compact low-dimensional representation such that on top of the representation we can …
Flambe: Structural complexity and representation learning of low rank mdps
In order to deal with the curse of dimensionality in reinforcement learning (RL), it is common
practice to make parametric assumptions where values or policies are functions of some low …
practice to make parametric assumptions where values or policies are functions of some low …