Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective change through time, or where all the training data and …
distribution and learning objective change through time, or where all the training data and …
A definition of continual reinforcement learning
In a standard view of the reinforcement learning problem, an agent's goal is to efficiently
identify a policy that maximizes long-term reward. However, this perspective is based on a …
identify a policy that maximizes long-term reward. However, this perspective is based on a …
Continual learning as computationally constrained reinforcement learning
An agent that efficiently accumulates knowledge to develop increasingly sophisticated skills
over a long lifetime could advance the frontier of artificial intelligence capabilities. The …
over a long lifetime could advance the frontier of artificial intelligence capabilities. The …
Three dogmas of reinforcement learning
Modern reinforcement learning has been conditioned by at least three dogmas. The first is
the environment spotlight, which refers to our tendency to focus on modeling environments …
the environment spotlight, which refers to our tendency to focus on modeling environments …
[PDF][PDF] Continual learning for robotics
Continual learning (CL) is a particular machine learning paradigm where the data
distribution and learning objective changes through time, or where all the training data and …
distribution and learning objective changes through time, or where all the training data and …
Continual learning with deep architectures
V Lomonaco - 2019 - amsdottorato.unibo.it
Humans have the extraordinary ability to learn continually from experience. Not only we can
apply previously learned knowledge and skills to new situations, we can also use these as …
apply previously learned knowledge and skills to new situations, we can also use these as …
Continual learning: Tackling catastrophic forgetting in deep neural networks with replay processes
T Lesort - arXiv preprint arXiv:2007.00487, 2020 - arxiv.org
Humans learn all their life long. They accumulate knowledge from a sequence of learning
experiences and remember the essential concepts without forgetting what they have learned …
experiences and remember the essential concepts without forgetting what they have learned …
Single-Task Continual Offline Reinforcement Learning
S Gai, D Wang - arXiv preprint arXiv:2404.12639, 2024 - arxiv.org
In this paper, we study the continual learning problem of single-task offline reinforcement
learning. In the past, continual reinforcement learning usually only dealt with multitasking …
learning. In the past, continual reinforcement learning usually only dealt with multitasking …
Exploration from generalization mediated by multiple controllers
P Dayan - Intrinsically motivated learning in natural and artificial …, 2013 - Springer
Intrinsic motivation involves internally governed drives for exploration, curiosity, and play.
These shape subjects over the course of development and beyond to explore to learn and …
These shape subjects over the course of development and beyond to explore to learn and …
Apprentissage continu: S'attaquer à l'oubli foudroyant des réseaux de neurones profonds grâce aux méthodes à rejeu de données
T Lesort - 2020 - theses.hal.science
Humans learn all their life long. They accumulate knowledge from a sequence of learning
experiences and remember the essential concepts without forgetting what they have learned …
experiences and remember the essential concepts without forgetting what they have learned …