A survey on model-based reinforcement learning
Reinforcement learning (RL) interacts with the environment to solve sequential decision-
making problems via a trial-and-error approach. Errors are always undesirable in real-world …
making problems via a trial-and-error approach. Errors are always undesirable in real-world …
Model-based reinforcement learning: A survey
Sequential decision making, commonly formalized as Markov Decision Process (MDP)
optimization, is an important challenge in artificial intelligence. Two key approaches to this …
optimization, is an important challenge in artificial intelligence. Two key approaches to this …
Systematic review on deep reinforcement learning-based energy management for different building types
Owing to the high energy demand of buildings, which accounted for 36% of the global share
in 2020, they are one of the core targets for energy-efficiency research and regulations …
in 2020, they are one of the core targets for energy-efficiency research and regulations …
Ranked reward: Enabling self-play reinforcement learning for combinatorial optimization
Adversarial self-play in two-player games has delivered impressive results when used with
reinforcement learning algorithms that combine deep neural networks and tree search …
reinforcement learning algorithms that combine deep neural networks and tree search …
[КНИГА][B] The science of deep learning
I Drori - 2022 - books.google.com
The Science of Deep Learning emerged from courses taught by the author that have
provided thousands of students with training and experience for their academic studies, and …
provided thousands of students with training and experience for their academic studies, and …
High-accuracy model-based reinforcement learning, a survey
Deep reinforcement learning has shown remarkable success in the past few years. Highly
complex sequential decision making problems from game playing and robotics have been …
complex sequential decision making problems from game playing and robotics have been …
Monte-carlo tree search for efficient visually guided rearrangement planning
We address the problem of visually guided rearrangement planning with many movable
objects, ie, finding a sequence of actions to move a set of objects from an initial arrangement …
objects, ie, finding a sequence of actions to move a set of objects from an initial arrangement …
Deep model-based reinforcement learning for high-dimensional problems, a survey
Deep reinforcement learning has shown remarkable success in the past few years. Highly
complex sequential decision making problems have been solved in tasks such as game …
complex sequential decision making problems have been solved in tasks such as game …
Learning to design without prior data: Discovering generalizable design strategies using deep learning and tree search
Abstract Building an Artificial Intelligence (AI) agent that can design on its own has been a
goal since the 1980s. Recently, deep learning has shown the ability to learn from large …
goal since the 1980s. Recently, deep learning has shown the ability to learn from large …
Beyond games: a systematic review of neural Monte Carlo tree search applications
The advent of AlphaGo and its successors marked the beginning of a new paradigm in
playing games using artificial intelligence. This was achieved by combining Monte Carlo …
playing games using artificial intelligence. This was achieved by combining Monte Carlo …