[HTML][HTML] Transfer learning in demand response: A review of algorithms for data-efficient modelling and control
A number of decarbonization scenarios for the energy sector are built on simultaneous
electrification of energy demand, and decarbonization of electricity generation through …
electrification of energy demand, and decarbonization of electricity generation through …
Comprehensive survey of machine learning approaches in cognitive radio-based vehicular ad hoc networks
Nowadays, machine learning (ML), which is one of the most rapidly growing technical tools,
is extensively used to solve critical challenges in various domains. Vehicular ad hoc network …
is extensively used to solve critical challenges in various domains. Vehicular ad hoc network …
Behavioral cloning from observation
Humans often learn how to perform tasks via imitation: they observe others perform a task,
and then very quickly infer the appropriate actions to take based on their observations. While …
and then very quickly infer the appropriate actions to take based on their observations. While …
[图书][B] Lifelong machine learning
Lifelong Machine Learning, Second Edition is an introduction to an advanced machine
learning paradigm that continuously learns by accumulating past knowledge that it then …
learning paradigm that continuously learns by accumulating past knowledge that it then …
Learning invariant feature spaces to transfer skills with reinforcement learning
People can learn a wide range of tasks from their own experience, but can also learn from
observing other creatures. This can accelerate acquisition of new skills even when the …
observing other creatures. This can accelerate acquisition of new skills even when the …
Transfer learning
Transfer learning is the improvement of learning in a new task through the transfer of
knowledge from a related task that has already been learned. While most machine learning …
knowledge from a related task that has already been learned. While most machine learning …
[PDF][PDF] Transfer learning for reinforcement learning domains: A survey.
The reinforcement learning paradigm is a popular way to address problems that have only
limited environmental feedback, rather than correctly labeled examples, as is common in …
limited environmental feedback, rather than correctly labeled examples, as is common in …
Transfer learning
SJ Pan - Learning, 2020 - api.taylorfrancis.com
Supervised machine learning techniques have already been widely studied and applied to
various real-world applications. However, most existing supervised algorithms work well …
various real-world applications. However, most existing supervised algorithms work well …
Transfer in reinforcement learning: a framework and a survey
A Lazaric - Reinforcement Learning: State-of-the-Art, 2012 - Springer
Transfer in reinforcement learning is a novel research area that focuses on the development
of methods to transfer knowledge from a set of source tasks to a target task. Whenever the …
of methods to transfer knowledge from a set of source tasks to a target task. Whenever the …
[PDF][PDF] Combining manual feedback with subsequent MDP reward signals for reinforcement learning.
As learning agents move from research labs to the real world, it is increasingly important that
human users, including those without programming skills, be able to teach agents desired …
human users, including those without programming skills, be able to teach agents desired …